Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs), offering biological plausibility and energy efficiency. Despite these merits, SNNs are frequently hampered by limited capacity and insufficient representation power, yet remain underexplored in remote sensing image (RSI) super-resolution (SR) tasks. In this paper, we first observe that spiking signals exhibit drastic intensity variations across diverse textures, highlighting an active learning state of the neurons. This observation motivates us to apply SNNs for efficient SR of RSIs. Inspired by the success of attention mechanisms in representing salient information, we devise the spiking attention block (SAB), a concise yet effective component that optimizes membrane potentials through inferred attention weights, which, in turn, regulates spiking activity for superior feature representation. Our key contributions include: 1) we bridge the independent modulation between temporal and channel dimensions, facilitating joint feature correlation learning, and 2) we access the global self-similar patterns in large-scale remote sensing imagery to infer spatial attention weights, incorporating effective priors for realistic and faithful reconstruction. Building upon SAB, we proposed SpikeSR, which achieves state-of-the-art performance across various remote sensing benchmarks such as AID, DOTA, and DIOR, while maintaining high computational efficiency. Code of SpikeSR will be available at https://github.com/XY-boy/SpikeSR.

Deep learning has achieved promising progress for digital elevation model (DEM) super-resolution (SR). However, the existing methods rarely consider the integration of multi-modal data with auxiliary high-frequency information. A primary challenge stems from the heterogeneous feature representations among these data sources, which complicates the effective learning of the terrain feature mapping relationships. In this paper, we propose a novel framework for DEM SR by integrating optical remote sensing imagery as the auxiliary data. A terrain-guided texture-edge feature fusion network is constructed to transfer the feature representation of high-resolution image textures with the guidance of informative terrain features, for adapting DEM SR learning. By exploiting the multi-dimensional attention mechanism, the meaningful components from the image conforming to the terrain features provide high-frequency information for DEM SR, while noisy features related to spectral variations are excluded from modelling. The terrain-oriented textural and edge features are then fused to generate the SR result with the constraint of a terrain feature-aware loss function. Extensive experiments on both simulated and real datasets indicate that the proposed method can reconstruct a DEM with high-accuracy elevation and sharper terrain details, and outperforms the state-of-art methods.

In the above article, there exists a citation error related to the core technical foundation of the proposed method. Reference [1] was incorrectly cited. The correct citation is reference [2].

Hyperspectral images (HSIs) often suffer from various noises, such as Gaussian noise, stripe noise, impulse noise, and deadlines due to the influence of sensors and external environments. These noises significantly degrade the quality of HSI and hinder subsequent applications. While most current transformer-based methods can effectively remove certain types of noise, they struggle with wide stripe noise. In addition, transformers are typically applied within local windows due to the limitation of computational complexity. Although windowshifting operations enhance the interaction between windows to a certain extent, this interaction remains insufficient for comprehensive global modeling. In light of these limitations, we propose a Bidirectional-aware network combining Transformer and Mamba (BTMnet), which consists of Bidirectional Long-Short Distance Attention (BLSDA) and Channel-Split Mamba (CSM). To better remove wide stripe noise, BLSDA is designed with two rectangular windows adapted to wide stripes in both vertical and horizontal directions, utilizing transformers to compute attention relationships within windows and across different windows. To further integrate global information and enhance the interaction of features between adjacent windows, CSM extracts global features by scanning in four directions across different feature channels. In BLSDA, we applied bidirectional windows in vertical and horizontal directions, and in CSM, we conducted bidirectional scanning in vertical and horizontal directions. The combination of these techniques allows for the simultaneous extraction of bidirectional features from HSI. By evaluating the metrics and visualization, the experimental results on simulated and real experiments prove that our method can achieve better results.

Satellite video object tracking (SVOT) delivers comprehensive spatiotemporal insights for Earth surface observation, yet existing SVOT methods confront several critical challenges including data scarcity, modality restrictions, paradigm gaps, and underutilization of multidimensional features, sealing the performance ceiling. This study proposes STAR, a unified spatiotemporal fusion framework for satellite video object tracking, mitigating these issues. To optimize satellite video scenes, STAR first introduces a scene enhancement module for generating enhanced multi-modal representations. Then, the extraction-correlation-adaptation module is designed, incorporating a multi-modal hierarchical Transformer architecture with local and unified relation modeling, which jointly achieves feature extraction, relation learning, and domain adaptation. Additionally, the temporal decoding structure is introduced to integrate deep temporal features via attention propagation. Finally, the inertial navigation module models physical temporal features, including an awareness selector to assess the tracking confidence-uncertainty and an inertial navigation scheme to manage anomalous interferences and continuous trajectory. Inspired by the prompt learning pattern, STAR introduces a minimal number of tunable parameters yet achieves competitive performance across various SVOT benchmarks. Implementation details and evaluation results will be available at: https://github.com/YZCU/STAR.

Accurate snow depth (SD) data are essential for understanding and simulating hydrological processes, particularly in regions with uneven snow distribution and complex terrain, such as the Third Pole (TP). However, existing SD products often suffer from low spatial resolution, introducing uncertainties in regional snowpack and runoff modeling. In this study, we developed an integrated downscaling framework that combines deep learning (DL) with physical constraints to improve the resolution and accuracy of SD data for the TP region. First, a residual network incorporating multifactor spatial-terrain relationships is used to generate preliminary 500-m DL-downscaled SD data. Subsequently, by incorporating both snow cover fraction (SCF) data and snow depletion curve (SDC) as dual physical constraints, the new algorithm significantly improves the estimation accuracy in shallow snow areas. The proposed algorithm significantly enhances SD estimation accuracy, reducing the root-mean-square error (RMSE) from 1.99 to 0.75 cm, which represents a 62% improvement over the purely DL-based downscaling algorithm (RMSE = 1.02 cm). Notably, the improved algorithm demonstrates enhanced capability in terrain feature representation while simultaneously minimizing misclassification in snow-free areas and reducing overestimation in shallow snow regions. The results demonstrate progressive improvement in SD estimation accuracy across diverse geographical environments through stepwise validation of each downscaling step, confirming that integrating DL-based downscaling with physical constraints yields complementary advantages. Although the new algorithm significantly improves the accuracy of SD data, it is still underestimated in complex terrain areas and areas with SD exceeding 7 cm. Future research needs to deeply integrate physical constraint methods, comprehensively consider physical processes such as snow density, water content changes, and energy balance, and establish a more complete downscaling model.

Polarimetric synthetic aperture radar (SAR) can capture rich polarization information of targets, but it is inherently affected by speckle. Learning-based methods have demonstrated superior speckle suppression potential. Most existing methods use optical images to simulate SAR noise for model training. Because of the significant differences in the imaging mechanisms between optical and SAR images, the data characteristics of these two types differ significantly, resulting in poor generalization performance. To this end, an Information Flow Switching-based Despeckling Network (IFSDN) is proposed for dual-polarization SAR image. By using the long time series data, the first dual-polarization SAR real dataset is constructed. The hybrid feature extraction module (HFEM) is constructed to independently extract and integrate features from both the diagonal and nondiagonal elements of the covariance matrix. Additionally, the multihierarchical residual attention despeckling (MRAD) module performs despeckling on feature maps from low to high levels. On this basis, the information flow switching mechanism facilitates the interaction of dominant features before and after despeckling, injecting spatial details into the despeckled results, reducing speckle noise, and preserving polarization information. By considering temporal changes, an adaptive joint loss function is, furthermore, constructed to guide the network training process, achieving high-fidelity despeckling while maintaining spatial-polarization information. Experiments show that IFSDN outperforms existing state-of-the-art methods in the speckle removal task for real dual-polarization SAR images, which can effectively preserve spatial and polarization information while suppressing speckles. Besides, generalization experiments demonstrate that the proposed model can be effectively applied to diverse datasets across various climate zones, showcasing its strong robustness.

Synthetic aperture radar (SAR) images are inherently affected by speckle noise due to their imaging principles, significantly impacting downstream research. Denoising methods based on deep learning have garnered attention and are gradually maturing, yet they face certain challenges. Denoising single SAR images lacks temporal information, often resulting in structural fitting that introduces artifacts. The existing denoising techniques do not adequately account for the differences between SAR and optical images, despite their distinct structural characteristics. In addition, mainstream deep learning approaches heavily rely on data-driven methods, with limited consideration for statistical properties. In response to these challenges, we propose a novel network for denoising SAR images fusing multitemporal interaction with coherence prior, termed MTCSAR. The proposed network leverages multitemporal interaction (MTI) to gather information from different moments in various regions. The redundancy across these temporal dimensions helps mitigate artifacts and edge blurring. To address SAR image characteristics, we design a dual-branch network combining global and local information to handle large-scale regions and fine structures. In addition, we incorporate prior coherence information from SAR images into the network, utilizing statistical properties to enhance transparency during training. Experimental results on simulated and real datasets demonstrate that injecting MTIs and coherence improves our method’s qualitative and quantitative performance, surpassing current state-of-the-art algorithms. This validates the effectiveness of the proposed MTCSAR for multitemporal SAR image denoising.

Monitoring particulate matter (PM) is of critical importance due to its significant impact on human health. Ground stations provide highly accurate measurements of various pollutants on a local scale. However, the limited distribution of these stations makes achieving global coverage challenging. To address this limitation, satellite imagery serves as a valuable resource, offering wide-area PM estimates in near real-time through abundant data and frequent revisit intervals. In contrast to other studies, this work introduces deep learning (DL) models to estimate ground-level PM concentration maps over Europe. These models rely exclusively on radiance data from the Sentinel-5P satellite, forgoing auxiliary information, such as meteorological data, which are commonly incorporated in similar studies. The proposed approach has demonstrated both robust estimation accuracy and effective generalization capabilities. Furthermore, the estimated PM concentration maps have been validated against ground-based measurements, showing superior performance with respect to widely used models and datasets that consider meteorological inputs. The dataset and the code are available here:https://github.com/antoniomazza88/PMUnet.

Image super-resolution (SR) is an effective way to enhance the spatial resolution and detail information of remote sensing images to obtain a superior visual quality. As SR is severely ill-conditioned, effective image priors are necessary to regularize the solution space and generate the corresponding high-resolution (HR) image. In this article, we propose a novel gradient-guided multiframe SR (MFSR) framework for remote sensing imagery reconstruction. The framework integrates a learned gradient prior as the regularization term into a model-based optimization method. Specifically, the local gradient regularization (LGR) prior is derived from the deep residual attention network (DRAN) through gradient profile transformation (GPT). The nonlocal total variation (NLTV) prior is characterized using the spatial structure similarity of the gradient patches with the maximum a posteriori (MAP) model. The modeled prior performs well in preserving edge smoothness and suppressing visual artifacts, while the learned prior is effective in enhancing sharp edges and recovering fine structures. By incorporating the two complementary priors into an adaptive norm-based reconstruction framework, the mixed L1 and L2 regularization minimization problem is optimized to achieve the required HR remote sensing image. Extensive experimental results on remote sensing data demonstrate that the proposed method can produce visually pleasant images and is superior to several of the state-of-the-art SR algorithms in terms of the quantitative evaluation.

Hyperspectral video (HSV) provides rich spectral-spatial-temporal information, enabling the capture of complex object dynamics beyond the limitations of conventional single- and multi-modal tracking. However, current HSV tracking methods face challenges such as data scarcity, band gaps, spectral fragmentation, temporal underutilization, and high computational load, which constrain performance. In this article, we present SpectralTrack, a novel HSV tracking framework with spectral-spatial fusion and memory enhancement. SpectralTrack incorporates an explicit visual prompting module to mitigate band gaps and spectral fragmentation. We further introduce an extraction-matching-interaction module, which leverages a template-bridging search adapter and a multi-layer perceptron adapter within a multi-modal Transformer architecture for efficient cross-modal feature extraction-matching-interaction. Additionally, a memory perception module enhances state reasoning by injecting temporal prompts to refine spectral and spatial cues. SpectralTrack follows parameter-efficient fine-tuning and feature-level fusion to alleviate data scarcity and reduce computational overhead. We instantiate two variants, SpectralTrack and SpectralTrack+, across nine HSV tracking datasets, demonstrating superior effectiveness over extensive trackers. Implementations and results will be available at https://github.com/YZCU/SpectralTrack.

Change detection(CD) is important for Earth observation, emergency response and time-series understanding. Recently, data availability in various modalities has increased rapidly, and multimodal change detection (MCD) is gaining prominence. Given the scarcity of datasets and labels for MCD, unsupervised approaches are more practical for MCD. However, previous methods typically either merely reduce the gap between multimodal data through transformation or feed the original multimodal data directly into the discriminant network for difference extraction. The former faces challenges in extracting precise difference features. The latter contains the pronounced intrinsic distinction between the original multimodal data; direct extraction and comparison of features usually introduce significant noise, thereby compromising the quality of the resultant difference image. In this article, we proposed the MaCon framework to synergistically distill the common and discrepancy representations. The MaCon framework unifies mask reconstruction (MR) and contrastive learning (CL) self-supervised paradigms, where the MR serves the purpose of transformation while CL focuses on discrimination. Moreover, we presented an optimal sampling strategy in the CL architecture, enabling the CL subnetwork to extract more distinguishable discrepancy representations. Furthermore, we developed an effective silent attention mechanism that not only enhances contrast in output representations but stabilizes the training. Experimental results on both multimodal and monomodal datasets demonstrate that the MaCon framework effectively distills the intrinsic common representations between varied modalities and manifests state-of-the-art performance across both multimodal and monomodal CD. Such findings imply that the MaCon possesses the potential to serve as a unified framework in the CD and relevant fields. Source code will be publicly available once the article is accepted.

How to aggregate spatial-temporal information plays an essential role in video super-resolution (VSR) tasks. Despite the remarkable success, existing methods adopt static convolution to encode spatial-temporal information, which lacks flexibility in aggregating information in large-scale remote sensing scenes, as they often contain heterogeneous features (e.g., diverse textures). In this paper, we propose a spatial feature diversity enhancement module (SDE) and channel diversity enhancement module (CDE), which explore the diverse representation of different local patterns while aggregating the global response with compactly channel-wise embedding representation. Specifically, SDE introduces multiple learnable filters to extract representative spatial variants and encodes them to generate a dynamic kernel for enriched spatial representation. To explore the diversity in the channel dimension, CDE exploits the discrete cosine transform to transform the feature into the frequency domain. This enriches the channel representation while mitigating massive frequency loss caused by pooling operation. Based on SDE and CDE, we further devise a multi-axis feature diversity enhancement (MADE) module to harmonize the spatial, channel, and pixel-wise features for diverse feature fusion. These elaborate strategies form a novel network for satellite VSR, termed MADNet, which achieves favorable performance against state-of-the-art method BasicVSR++ in terms of average PSNR by 0.14 dB on various video satellites, including JiLin-1, Carbonite-2, SkySat-1, and UrtheCast. Code will be available at https://github.com/XY-boy/MADNet.

Recent progress in remote sensing image (RSI) super-resolution (SR) has exhibited remarkable performance using deep neural networks, e.g., Convolutional Neural Networks and Transformers. However, existing SR methods often suffer from either a limited receptive field or quadratic computational overhead, resulting in sub-optimal global representation and unacceptable computational costs in large-scale RSI. To alleviate these issues, we develop the first attempt to integrate the Vision State Space Model (Mamba) for RSI-SR, which specializes in processing large-scale RSI by capturing long-range dependency with linear complexity. To achieve better SR reconstruction, building upon Mamba, we devise a Frequency-assisted Mamba framework, dubbed FMSR, to explore the spatial and frequent correlations. In particular, our FMSR features a multi-level fusion architecture equipped with the Frequency Selection Module (FSM), Vision State Space Module (VSSM), and Hybrid Gate Module (HGM) to grasp their merits for effective spatial-frequency fusion. Considering that global and local dependencies are complementary and both beneficial for SR, we further recalibrate these multi-level features for accurate feature fusion via learnable scaling adaptors. Extensive experiments on AID, DOTA, and DIOR benchmarks demonstrate that our FMSR outperforms state-of-the-art Transformer-based methods HAT-L in terms of PSNR by 0.11 dB on average, while consuming only 28.05% and 19.08% of its memory consumption and complexity, respectively.

Transformer-based super-resolution (SR) method has recently demonstrated promising performance, due to its long-range and global aggregation capability. However, the existing Transformer brings two critical challenges for applying it in large-area earth observation scenes: (1) redundant token representation due to most irrelevant tokens; (2) single-scale representation which ignores scale correlation modeling of similar ground observation targets. To this end, this paper proposes to adaptively eliminate the interference of irreverent tokens for a more compact self-attention calculation. Specifically, we devise a Residual Token Selective Group (RTSG) to grasp the most crucial token by dynamically selecting the top-k keys in terms of score ranking for each query. For better feature aggregation, a Multi-scale Feed-forward Layer (MFL) is developed to generate an enriched representation of multi-scale feature mixtures during the feed-forward process. In particular, multiple cascaded RTSGs form our final Top-k Token Selective Transformer (TTST) to achieve progressive representation. Extensive experiments on three remote sensing benchmarks demonstrate our TTST performs favorably against state-of-the-art CNN-based and Transformer-based methods, both qualitatively and quantitatively.

Semantic change detection, which aims to locate land cover changes and identify their categories using pixel-level boundaries, has promising applications in Earth vision, including precise urban planning and natural resource management. This paper proposes a novel Siamese U-Net architecture for semantic change detection in synthetic aperture radar (SAR) images, incorporating a residual network with weight-sharing as the backbone network. The network is capable of simultaneously yielding binary change detection and semantic change detection results. Additionally, we have designed a statistic ratio attention module that utilizes statistical features from the original image as spatial ratio attention, coupled with channel attention, to extract change information from the bi-temporal SAR images. Furthermore, as there is currently no existing dataset for semantic change detection in SAR images, we have constructed a dedicated dataset to facilitate model training and evaluation. Our experiment results demonstrate the superiority of our proposed model over other comparison algorithms.

Semi-empirical models and machine learning models have been widely used in remote sensing studies. In order to explore the feasible joint mode integrating semi-empirical model and machine learning algorithm, two distinct joint modes, cycle and series mode and deep and parallel mode, were designed and evaluated to synthesize the respective advantages of the two models to complete high-resolution soil moisture retrieval. Cycle and series mode improved the generalization ability of the retrieval models in the case of less labeled data, and enhanced its physical interpretability. The deep and parallel mode improved the accuracy of the retrieval models in a wider range of application with estimation accuracy of 0.754 and 0.071 m3·m-3in terms of coefficient of determination and unbiased root mean square error in site-based validation. The joint modes constructed in this study provide ideas for subsequent studies on model fusion and improving the physical interpretability of machine learning models.

As a significant element in the water cycle and a key parameter associated with atmospheric circulation, precipitation requires to be fast and accurately monitored. In this study, a new physics-informed multi-temporal model (PMDF) is proposed for the estimates of near real-time high-resolution (0.02°) precipitation, which adopts an ensemble learning framework. The PMDF model can introduce physical knowledge into itself and fast generate precipitation during the estimating phase with no auxiliary data. Validation results show that the PMDF model performs well, with the CC (CSI) of 0.44 (0.49) and 0.73 (0.63) at hourly and daily scales, respectively. The designed physics-informed and multi-temporal strategies both can effectively improve the model accuracy, which yields a significantly better performance than other widely used precipitation products. Furthermore, we can clearly observe the hourly variations of precipitation from estimated results at a high spatial resolution.

Blurring and noise degrade the performance of image processing. To mitigate this effect, various regularization-based deblurring methods have been proposed. Total variation regularization is widely used owing to its excellent ability in preserving the salient edges, but it also tends to smooth the image details. In this paper, we propose a local extremum-constrained total variation (LECTV) framework for image deblurring. In the developed deblurring framework, we integrate prior knowledge of the dark channel with the structural features of the image into a single regularization term. Furthermore, unlike most existing methods that focus on the overall sparsity of the dark channel, the defined regularization term allows for a pixel-wise adaptive description of the image to restore its inherent spatial texture structure. Finally, a majorization-minimization-based method is designed to solve the developed LECTV framework. Experimental results on natural and hyperspectral images show that the designed framework exhibits excellent performance in removing multiple types and degrees of blurring. Extensive evaluations also further show its superiority compared to other advanced methods.

Optical-flow-based and kernel-based approaches have been extensively explored for temporal compensation in satellite Video Super-Resolution (VSR). However, these techniques are less generalized in large-scale or complex scenarios, especially in satellite videos. In this paper, we propose to exploit the well-defined temporal difference for efficient and effective temporal compensation. To fully utilize the local and global temporal information within frames, we systematically modeled the short-term and long-term temporal discrepancies since we observe that these discrepancies offer distinct and mutually complementary properties. Specifically, we devise a Short-term Temporal Difference Module (S-TDM) to extract local motion representations from RGB difference maps between adjacent frames, which yields more clues for accurate texture representation. To explore the global dependency in the entire frame sequence, a Long-term Temporal Difference Module (L-TDM) is proposed, where the differences between forward and backward segments are incorporated and activated to guide the modulation of the temporal feature, leading to a holistic global compensation. Moreover, we further propose a Difference Compensation Unit (DCU) to enrich the interaction between the spatial distribution of the target frame and temporal compensated results, which helps maintain spatial consistency while refining the features to avoid misalignment. Rigorous objective and subjective evaluations conducted across five mainstream video satellites demonstrate that our method performs favorably against state-of-the-art approaches. Code will be available athttps://github.com/XY-boy/LGTD.

Hyperspectral (HS) video captures continuous spectral information of objects, enhancing material identification in tracking tasks. It is expected to overcome the inherent limitations of red-green–blue (RGB) and multimodal tracking, such as finite spectral cues and cumbersome modality alignment. However, HS tracking faces challenges such as data anxiety, bandgaps, and huge volumes. In this study, inspired by prompt learning in language models, we propose the prompting for hyperspectral video tracking (PHTrack) framework. PHTrack learns prompts to adapt foundation models, mitigating data anxiety and enhancing performance and efficiency. First, the modality prompter (MOP) is proposed to capture rich spectral cues and bridge bandgaps for improved model adaptation and knowledge enhancement. In addition, the distillation prompter (DIP) is developed to refine cross-modal features. PHTrack follows feature-level fusion, effectively managing huge volumes compared to traditional decision-level fusion fashions. Extensive experiments validate the proposed framework, offering valuable insights for future research. The code and data will be available athttps://github.com/YZCU/PHTrack

Hyperspectral (HS) video is able to capture abundant spectral, spatial, and temporal information about objects, which overcomes the limitations of common red-green-blue (RGB) video in complex scenarios such as similar appearances and background clutters (BCs). However, most trackers apply hand-crafted features extracted from manually selected bands instead of deep features for object representations due to limited HS data and the band gap problem. Each HS image consists of many bands, and it is challenging to fully interact with the band information while maintaining tracking speed. To this end, this article proposes a novel end-to-end spectral awareness interaction network with a dynamic template (SPIRIT) for HS video object tracking. First, a spectral awareness module (SAM) is proposed to learn band contributions with consideration of nonlinear and global interactions between HS bands. It can also cooperate with the feature extraction module pretrained with RGB data to attenuate the band gap and data-hungry. Second, an interaction module (IM) is proposed to achieve inter and intraband feature interactions to enhance tracking performance while improving efficiency. Furthermore, the proposed method contains a novel update module (UM) that evaluates the tracking confidence of the current state to adapt to object changes and attenuate tracking drifts. Extensive experiments demonstrate the superiority of our approach compared to state-of-the-arts (SOTAs) while meeting real-time demands.

Hyperspectral image (HSI) clustering is a fundamental yet challenging task that groups image pixels with similar features into distinct clusters. Among various approaches, contrastive learning methods, which employ the concept of encouraging semantically similar samples to move closer together while pushing semantically inconsistent samples apart, have garnered significant attention due to their promising performance. However, the most prevalent approaches face two major limitations: 1) treating all samples indiscriminately during optimization, where the abundance of well-categorized samples overwhelms the feature learning process and 2) tending to introduce noise when constructing positive sample pairs through view augmentation or searching the nearest neighbors, which would cause semantic drift of sample features. To solve these issues, we propose a graph autoencoder-based deep clustering framework named spatial–spectral graph contrastive clustering with hard sample mining (SSGCC) that constructs spatial–spectral dual views without data augmentation and focuses more on hard samples rather than treating all samples equally with the aid of spatial–spectral features. Concretely, we extract the spectral features and the neighborhood spatial features of the samples as dual branches to avoid the noise caused by data augmentation and develop the cluster-oriented consistency learning to facilitate the exchange of knowledge between the two spectral–spatial perspectives. In addition, we propose a hard sample mining-based contrastive learning scheme with the aid of spatial–spectral features. To better measure the importance of the samples, we combine spatial features and spectral features to calculate the similarity between sample pairs. The weights of hard sample pairs are dynamically up-weight while the easy ones are down-weighting to improve the discriminative capability. Extensive experiments on four benchmark HSI datasets demonstrate the effectiveness and superiority of the proposed methods against state-of-the-art ones.

Supervised deep learning despeckling methods usually use optical images to simulate multiplicative noise for training. However, due to the different imaging mechanisms of optical images and SAR images, the data characteristics of the two are significantly different, resulting in poor generalization performance of the model trained through the above form. Besides, the existing deep learning models do not fully consider the physical scattering mechanism, which causes the loss of polarization information. To solve those problems, an unsupervised deep learning method is proposed for dual-polarization SAR image despeckling. Under this framework, we combine the dual-polarization SAR covariance matrix and polarization decomposition information to construct a Dual-branch SAR image Despeckling Network (DSDN). The residual channel and the spatial attention mechanism are embedded to calibrate the polarization and spatial feature maps. The cross-attention mechanism is designed to mine the association of feature maps before and after denoising. Besides, the dual-branch joint loss function is proposed to constrain the training process. Spatial information experiments and polarization information experiments indicate that, compared with the existing state-of-the-art SAR despeckling methods, the proposed method can effectively remove the coherent speckle noise of dual-polarization SAR images, and can better preserve the polarization information. Codes are available at https://github.com/LiupengLin/DSDN.

With the development of satellite videos, multiobject tracking (MOT) in satellite video is possible and has become a new challenging task. The challenges of the task are mainly from the characteristics of objects in satellite videos: 1) small objects; 2) similarity of objects and background; 3) sparse object distribution; and 4) various data quality. These characteristics make it difficult to apply MOT methods for ground-based video directly to satellite video. To tackle the problem, this article proposes a more focused and continuous two-stage satellite video MOT method named FoCos. In the detection part, a multiscale center-point-based small object detector assisted by multitype dynamic data is introduced. This detector uses the dynamic characteristics of vehicles and their correlation with the background to better localize the vehicles. Meanwhile, considering that vehicles are sparsely distributed, a feature de-redundancy convolution (FDC) module is applied within the detector. In the tracking part, considering the problem of poor generalization caused by an uneven quality between data, a quality-aware confidence hierarchy (QACH) module, as well as a two-stage data association module, is introduced to perceive the quality of the data and separate confidence into hierarchies for the association, improving the performance of the method under the different quality of videos. Meanwhile, a trajectory smoothing (TS) strategy is introduced to obtain more continuous trajectories. Finally, the proposed method is evaluated in three satellite video datasets and its superiority is demonstrated.

Hyperspectral images (HSIs) with rich spectral information have been widely used in surface classification, object detection, and other real application problems. However, due to the hardware limitations, the low spatial resolution HSIs hinder the exploration of their application potential. Deep learning-based methods are currently the most common solutions for single HSI super-resolution (HSI SR) tasks. However, such methods often overlook the degradation principle from high-resolution HSI to low-resolution HSI. In this article, we propose a CNN-transformer embedded unfolding network (CTUNet), in which an unfolding framework with an effective spatial-spectral prior network is designed for HSI SR by incorporating the degradation principle of HSIs. Specifically, a maximum posterior-based energy model is employed, enabling alternate optimization to seek the optimal solution in an iterative mechanism. To effectively utilize the structure prior of HSI, multiscale self-calibrated convolution (MSSC) and edge-guided transformer module are combined to learn latent spatial-spectral priors. Additionally, hidden feature connections between adjacent iterations enhance the representation of the image features. Extensive experiments conducted on three available HSI datasets demonstrate that our method outperforms several state-of-the-art HSI SR methods. The code will be available athttps://github.com/YoeTon/CTUNet.

Sudden natural disasters and man-made disasters pose a threat to human life and property safety, and real-time semantic segmentation of high-resolution remote sensing images is crucial for disaster damage assessment applications. In recent years, with the wide application of high spatial resolution (HSR) remote sensing images and semantic change detection methods based on deep learning (DL), the acquisition of information on damaged areas has become more and more convenient and accurate. However, due to the black box characteristics of existing methods, the lack of interpretability and prior knowledge embedding (such as building positioning information), as well as the low utilization of damage conditions around the building, lead to automatically learned feature representations that still need to be improved. To solve these problems, we proposed the position-constrained differential attention siamese network (PCDASNet). The main idea is to merge building extraction and disaster damage assessment into a cascaded framework to improve building damage recognition results under the constraints of building positioning information. In particular, the proposed Differential Attention Module (DAM) adaptively extracts change information corresponding to buildings and surrounding environments from dual-temporal images, with interpretability and theoretical guarantee, which enables the integration of prior positioning knowledge into the design of network architecture. The objective metrics of the method on the building damage dataset show that the method achieves a test F1 score of more than 73% compared with other baseline methods, and also outperforms several state-of-the-art methods in terms of visual results.

Recently, convolutional networks have achieved remarkable development in remote sensing image (RSI) super-resolution (SR) by minimizing the regression objectives, e.g., MSE loss. However, despite achieving impressive performance, these methods often suffer from poor visual quality with oversmooth issues. Generative adversarial networks (GANs) have the potential to infer intricate details, but they are easy to collapse, resulting in undesirable artifacts. To mitigate these issues, in this article, we first introduce diffusion probabilistic model (DPM) for efficient RSI SR, dubbed efficient diffusion model for RSI SR (EDiffSR). EDiffSR is easy to train and maintains the merits of DPM in generating perceptual-pleasant images. Specifically, different from previous works using heavy UNet for noise prediction, we develop an efficient activation network (EANet) to achieve favorable noise prediction performance by simplified channel attention and simple gate operation, which dramatically reduces the computational budget. Moreover, to introduce more valuable prior knowledge into the proposed EDiffSR, a practical conditional prior enhancement module (CPEM) is developed to help extract an enriched condition. Unlike most DPM-based SR models that directly generate conditions by amplifying LR images, the proposed CPEM helps to retain more informative cues for accurate SR. Extensive experiments on four remote sensing datasets demonstrate that EDiffSR can restore visual-pleasant images on simulated and real-world RSIs, both quantitatively and qualitatively. The code of EDiffSR will be available athttps://github.com/XY-boy/EDiffSR.

Hyperspectral image (HSI) denoising is a crucial step for its subsequent applications. In this article, we propose TDSAT, a 3-D spatial-spectral attention Transformer model designed to effectively remove noise in HSI processing while preserving essential spectral and spatial information. The primary objective of this model is to utilize the 3-D Transformer to explore the global spectral-spatial features in HSI, learn the relationships among different bands, and preserve high-quality spectral and spatial information for denoising. The proposed method consists of three main components: the multihead spectral attention (MHSA) module, the gated-dconv feedforward network (GDFN) module, and the spectral enhancement (SpeE) module. The MHSA module learns the relationships among different bands and emphasizes the local spatial information. The GDFN module explores more expressive and discriminative spectral features. The SpeE module enhances the perception of subtle differences between different spectrums. Moreover, unlike the previous Transformer denoising method that can only handle fixed bands, the proposed method combines 3-D convolution and spectral-spatial attention Transformer blocks, enabling the denoising of HSI with an arbitrary number of bands. Experimental results demonstrate that TDSAT outperforms compared methods. The code is available athttps://github.com/Featherrain/TDSAT.

Transformer-based method has demonstrated promising performance in image super-resolution tasks, due to its long-range and global aggregation capability. However, the existing Transformer brings two critical challenges for applying it in large-area earth observation scenes: (1) redundant token representation due to most irrelevant tokens; (2) single-scale representation which ignores scale correlation modeling of similar ground observation targets. To this end, this paper proposes to adaptively eliminate the interference of irreverent tokens for a more compact self-attention calculation. Specifically, we devise a Residual Token Selective Group (RTSG) to grasp the most crucial token by dynamically selecting the top- k keys in terms of score ranking for each query. For better feature aggregation, a Multi-scale Feed-forward Layer (MFL) is developed to generate an enriched representation of multi-scale feature mixtures during feed-forward process. Moreover, we also proposed a Global Context Attention (GCA) to fully explore the most informative components, thus introducing more inductive bias to the RTSG for an accurate reconstruction. In particular, multiple cascaded RTSGs form our final Top- k Token Selective Transformer (TTST) to achieve progressive representation. Extensive experiments on simulated and real-world remote sensing datasets demonstrate our TTST could perform favorably against state-of-the-art CNN-based and Transformer-based methods, both qualitatively and quantitatively. In brief, TTST outperforms the state-of-the-art approach (HAT-L) in terms of PSNR by 0.14 dB on average, but only accounts for 47.26% and 46.97% of its computational cost and parameters. The code and pre-trained TTST will be available at https://github.com/XY-boy/TTST for validation.

Road damage presents a significant risk to traffic safety, including pavement distress and disaster-induced damage. Thanks to their high efficiency, computer vision-based methods for pavement distress detection have been widely developed. In disaster scenarios, the automatic extraction of road damage information from extensive social media images plays a critical role in rescue efforts. However, few existing studies have focused on detecting object-level disaster-induced road damage. To fill the gap, this paper presents a Social media image dataset of Object detection for Disaster-induced Road damage (SODR), including 1,552 images and two categories (i.e., collapses and blockages). Additionally, this paper proposes an ensemble learning approach with attention mechanisms based on YOLOv5 (You Only Look Once) network. Initially, attention modules are employed to create two distinct detectors for ensemble learning. Subsequently, one standard YOLOv5 and two variant networks are trained with consistent settings, and test time augmentation is applied during the inference phase. The proposed method has been implemented across five scales of YOLOv5, offering alternatives for balancing accuracy and computational cost. To demonstrate the validity, comprehensive experiments were conducted on two datasets. Compared with some mainstream detectors and ensemble learning methods, our approach achieved competitive results with a fewer number of parameters and a simpler training and testing process. The SODR dataset and source code are available at (https://github.com/nonondayo/yolov5_SODRv1).

Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications. In this technical review, we first give the noise analysis in different noisy HSIs and conclude crucial points for programming HSI denoising algorithms. Then, a general HSI restoration model is formulated for optimization. Later, we comprehensively review existing HSI denoising methods, from model-driven strategy (nonlocal mean, total variation, sparse representation, low-rank matrix approximation, and low-rank tensor factorization), data-driven strategy [2-D convolutional neural network (CNN), 3-D CNN, hybrid, and unsupervised networks], to model-data-driven strategy. The advantages and disadvantages of each strategy for HSI denoising are summarized and contrasted. Behind this, we present an evaluation of the HSI denoising methods for various noisy HSIs in simulated and real experiments. The classification results of denoised HSIs and execution efficiency are depicted through these HSI denoising methods. Finally, prospects of future HSI denoising methods are listed in this technical review to guide the ongoing road for HSI denoising. The HSI denoising dataset could be found at https://qzhang95.github.io.

Soil moisture (SM) is a key state variable in hydrology, climatology and water resource management. Microwave remote sensing can retrieve soil moisture at regional or global scales, but limited by spatial resolution, it is difficult to accurately reflect the details of soil moisture. To overcome this limitation, in this paper, we propose a Soil Moisture Downscaling residual dense Network (SMDN) based on spatiotemporal information. We model the relationship between low-resolution geoscience parameters and soil moisture, and then downscale soil moisture products by applying the model to high-resolution geoscience parameters. On this basis, the residual correction is performed on the soil moisture downscaling results to generate high-precision soil moisture products. Visual evaluation and quantitative experiments show that the proposed network can effectively improve the spatial detail information, maintain the spatial distribution pattern, and have strong stability.

Satellite video is a novel data source for earth observation, which can be applied in multiple fields for dynamic monitoring. It is always equipped with high temporal resolution at the cost of low spatial resolution of tiny moving objects. Video super-resolution (VSR) is utilized to improve the spatial resolution of satellite video and obtain high spatial-temporal resolution data. However, most existing VSR methods mainly focus on the local interframe information during feature alignment, which lack the ability to model long-distance correspondence. In this letter, a novel two-branch alignment network with an efficient fusion module is proposed for satellite VSR. Both deformable convolution (DCN) and transformer-like attention are employed to fully explore the local and global information between frames. Furthermore, a fusion module is proposed to model the residuals between fusion features and compensate them for better fusion. Experiments on Jilin-1 satellite videos demonstrate that the proposed network can achieve comparable results to current state-of-the-art (SOTA) VSR methods with tiny parameters.

Mixed noise pollution severely disturbs hyperspectral image (HSI) processing and applications. Plenty of algorithms have been developed to address this issue via two strategies: model-driven or data-driven strategy. However, model-driven methods exist in the highly time-consuming weakness of iterative optimization and unstable sensitivity of setting parameters. Data-driven methods usually perform poor due to the overfitting effects. To solve these issues, we combine both the deep denoising priors with low-rank tensor factorization (DP-LRTF) for HSI restoration. The proposed method uses Tucker tensor factorization to depict the global spectral low-rank constraint. Then the spectral orthogonal basis and spatial reduced factor are optimized by two deep denoising priors, respectively. Through this integrated strategy, we can simultaneously exploit the intrinsic low-rank property of HSI, and utilize the powerful feature extraction ability by deep learning for HSI restoration. Compared with model-driven and data-driven methods, DP-LRTF outperforms on HSI mixed noise removal and execution efficiency for various simulated/real experiments.

Due to the limitations of sensor hardware devices, the hyperspectral image (HSI) often suffers from various types of noise, such as Gaussian noise, impulse noise, stripe noise, and deadlines, which can significantly degrade their quality. Although many data-driven methods have been proposed to deal with complex noise, few of them consider the structural characteristics of noise. This not only leads to a lack of interpretability but also results in poor performance when dealing with structural noise in practical applications. To address this issue, this article proposes KPInet, a convolutional neural network (CNN) driven by the structural knowledge of noise for HSI denoising. First and foremost, the knowledge optimization-driven module (KODM) utilizes the deep unrolling method to unfold a total variation (TV) algorithm that considers the structural characteristics of noise. This approach improves the network’s interpretability and results in better performance on structural noise, while maintaining the effect of removing Gaussian noise. Second, the statistical feature injection module (SFIM) extracts more features by utilizing spectral gradients, medians, and means of the HSI. Third, the multiscale degradation guidance module (MDGM) utilizes a dual-stream decoder with a low-resolution upsampling guidance branch to better distinguish the real structure and noise structure in the HSI. Experimental results on simulated and real datasets indicate that the approach achieves favorable denoising performance, as evidenced by both quantitative evaluation metrics and visual results. Furthermore, it also demonstrates the robustness and generalization capacity of the proposed KPInet.

Soil moisture is one of the important parameters in Earth system models. In recent years, the retrieval based on machine learning and data fusion of multi-source satellite observation data has become one of the effective methods to obtain soil moisture information at a large scale. However, most retrieval studies need to download remote sensing original data first, then preprocess, train the retrieval models, and finally generate products in the offline environment. In order to meet the requirements of long temporal series of large-scale area retrieval, and with the widespread use of machine learning in retrieval studies, the amount of remote sensing data and necessary computing resources are gradually increasing. Moreover, studies usually use a single machine learning retrieval model for the entire study area, which lacks the consideration of geographical differences and spatial heterogeneity of soil moisture. Therefore, we established a geo-intelligent soil moisture retrieval framework completely based on the cloud environment. In this study, a variety of machine learning algorithms were used to fuse multi-source observation data mainly including MODIS data and other auxiliary data, and the Continental United States (CONUS) was taken as the experimental area to generate soil moisture data with a resolution of 500m. In addition, this study combines geographical correlation with machine learning models to cope with the spatial heterogeneity of surface soil moisture. Overall, on the basis of site-based validation, the retrieval model trained under the framework performed well, with estimation accuracy of 0.716 and 0.0383 m3·m-3in terms of coefficient of determination (R2) and unbiased root mean square error (ubRMSE). The establishment of the cloud retrieval framework provides convenience for the whole retrieval process and also provides a new idea for other retrieval studies of geoscience parameters.

Recent efforts have witnessed remarkable progress in Satellite Video Super-Resolution (SVSR). However, most SVSR methods usually assume the degradation is fixed and known,e.g., bicubicdownsampling, which makes them vulnerable in real-world scenes with multiple and unknown degradations. To alleviate this issue, blind SR has thus become a research hotspot. Nevertheless, existing approaches are mainly engaged in blur kernel estimation while losing sight of another critical aspect for VSR tasks: temporal compensation, especially compensating for blurry and smooth pixels with vital sharpness from severely degraded satellite videos. Therefore, this paper proposes a practical Blind SVSR algorithm (BSVSR) to explore more sharp cues by considering the pixel-wise blur levels in a coarse-to-fine manner. Specifically, we employed multi-scale deformable convolution to coarsely aggregate the temporal redundancy into adjacent frames by window-slid progressive fusion. Then the adjacent features are finely merged into mid-feature using deformable attention, which measures the blur levels of pixels and assigns more weights to the informative pixels, thus inspiring the representation of sharpness. Moreover, we devise a pyramid spatial transformation module to adjust the solution space of sharp mid-feature, resulting in flexible feature adaptation in multi-level domains. Quantitative and qualitative evaluations on both simulated and real-world satellite videos demonstrate that our BSVSR performs favorably against state-of-the-art non-blind and blind SR models. Code will be available at https://github.com/XY-boy/Blind-Satellite-VSR.

High temporal resolution aerosol optical depth (AOD) observations derived from new-generation geostationary (GEO) satellite possess unique advantages in analyzing aerosol fast variation processes and thereby providing more accurate assessments on their climate effects and health risks. Unfortunately, the expected advantages and values are dramatically limited by relatively large proportion of data missing in the GEO AOD products due to cloud obscuration and intrinsic retrieval algorithm. Although several data recovery algorithms have been proposed in recent years to improve the spatial coverage for GEO AOD products, yet most of them aims at filling up the data blanks rather than reconstructing the temporally continuous variation of aerosol. Accordingly, in this study, a novel framework of nested spatiotemporal fusion blending GEO with sun-synchronous orbit (SSO) satellite observations based on Bayesian maximum entropy (BME) theorem is developed for GEO Advanced Himawari-8 Imager (AHI) AOD recovery with the sufficient excavation of complementary information from GEO and SSO satellite observations, where the minute-stage and hour-stage BME fusion are jointly employed to reconcile temporal inconsistency and data discrepancies between GEO and SSO observations. The results demonstrate that the AOD spatial coverage is dramatically increased by 240.9% (from 20.5% to 70%) with ensured accuracy after Nested-BME fusion. Additionally, two case analyses, during the development and dispersion processes of haze respectively, both demonstrate that the proposed Nested-BME fusion framework could reconstruct the reliable aerosol diurnal variation trends on the basis of recovering missing data for Himawari-8 AHI AOD datasets, while the AHI official level-2 and level-3 AOD products fail to capture these key trends. Furthermore, the developed Nested-BME AOD fusion framework is also applicable for other geostationary satellites over other regions, which could substantially enhance the availability and value of high temporal resolution AOD products for better scientific applications.

Road damage detection is significant for road maintenance. Traditional manual visual inspection methods consume lots of time and labor. Developments in the field of computer vision create opportunities for automated and efficient image-based road damage detection. Through deep convolution neural networks, road damage localization and classification can be achieved simultaneously. This paper proposes an ensemble model with test time augmentation based on the You Only Look Once (YOLOv5) network and attention modules. The approach utilizes a state-of-the-art object detector known as YOLOv5. To focus more on the road in images, five improved YOLOv5 models with attention modules are proposed. Moreover, ensemble learning and test time augmentation are adopted to improve model generalization and detection performance. The proposed method was evaluated through the IEEE Big Data Crowdsensing-based Road Damage Detection Challenge 2022. Different ensemble models achieved an average F1-score of 0.65177 on the five test datasets.

Particulate matter with a diameter of less than 2.5 microns (PM2.5) in the air is one of the most critical pollutants related to air quality. Exposure to high levels of PM2.5, which can be inhaled and carry harmful chemicals deep into the lungs and bloodstream, can have acute or chronic adverse health effects. A reliable, convenient and low-cost method to obtain PM2.5 concentration can help people improve their awareness of it. At the same time, it can also provide a certain reference for the prevention and control of haze, for air purification and for some other work to reduce the harm of air pollution to human health. In this paper, from the perspective of deep learning, with the help of photos that can be taken anywhere, we combined convolution neural network and support vector regression machine to estimate PM2.5. The method used in this paper is compared with other methods using the same dataset to prove its effectiveness.

Hyperspectral images(HSIs) captured from actual observations are inevitably interfered by different types of degradation, but existing methods are difficult to recover from corruptions with extensive coverage. To surmount these challenges, a hyperspectral image restoration method based on tensor av-erage rank and spatial-spectral total variation is proposed in this paper, which can be solved by the alternating direction method of multipliers (ADMM) algorithm. Extensive experi-ments on both synthetic and real-world data demonstrate that the model has a better ability to restore hyperspectral images from corruptions, such as deadlines and stripe noises.

Existing video super-resolution (VSR) methods usually merge the redundant temporal information along frames to achieve information enhancement, which naturally discards the spatial redundancy information. This paper proposes an intrinsic Graph Neural Network (GNN) framework for satellite VSR to fully explore the internal spatial prior while considering the temporal information in the video frame sequence. Firstly, a Multi-Scale Deformable convolution (MSD) is adopted to accurately model the spatial-temporal relationship between frames. Then, we search for k-nearest neighbors to construct the spatial graph and profoundly excavate the prior spatial information brought by patch recurrence. Finally, the spatial-temporal redundant information is integrated and complementary. Experiments on Jilin-1 satellite video demonstrate the effectiveness of our framework.

Passive microwave remote sensing can effectively capture the near-surface soil freeze/thaw onsets. Accurately understanding the transition of permafrost freeze/thaw state is helpful for us to respond to climate change in time. In order to improve the retrieval accuracy of freeze/thaw onsets, we propose an XGBoost modeling method that combines SMAP and AMSR2 for freeze/thaw onsets detection. We conducted experiments using data covering Alaska from 2015 to 2020 to demonstrate the effectiveness of our method. The proposed model was applied to the whole study area to obtain the spatial and temporal distribution of freezing periods. During the study period, the shortening of the freezing period has been most evident in 2018–2019. The variation of the freezing period is related to climate anomalies.

Air temperature (Ta) is one of the most fundamental and important variables in ecological and environmental science. In this work, we fuse the brightness temperature data of Himawari-8 and auxiliary data such as terrain, vegetation, time, and meteorological elements to develop a model based on the Light Gradient Boosting Machine (LightGBM). The hourly high-resolution Ta is estimated and the spatial distributions are mapped in China. The evaluation results show that the model performs well in the study area. Based on the five-fold cross-validation of all samples, the R2is 0.986 and the RMSE is 1.598 K. The spatial distribution patterns can well reflect the Ta changes in different regions of China.

In the field of remote sensing image pansharpening, deep learning-based methods have shown impressive performances recently. However, most deep learning-based pansharpening methods are based on supervised learning, which requires a large number of training images. In addition, obtaining large amounts of images with a high spatial and spectral resolution for training may be difficult in practice. In this letter, a novel self-supervised learning method based on a cycle-consistent generative adversarial network (CycleGAN) is proposed for remote sensing image pansharpening, without requiring large volumes of data for training. The framework contains two generators and two discriminators, and applies a residual neural network to the first generator. The panchromatic (PAN) image and multispectral (MS) image are input into the first generator to obtain the fused image, and then the fused image is input into the second generator to obtain a PAN image, which should be consistent with the input PAN image. The experimental results show that the proposed method performs better than the state-of-the-art unsupervised pansharpening method, and also achieves a competitive performance when compared with a supervised method.

Deep learning shows potential superiority in the image fusion field. To solve the problem of the spatial resolution degradation of polarimetric synthetic aperture radar (PolSAR) images caused by system limitation, we propose a fully PolSAR images and DualSAR images fusion network (FDFNet). We use low resolution (LR)-PolSAR super-resolution (LPSR) and modified cross attention mechanism (MCroAM) to perform data fusion on LR-PolSAR and high resolution (HR)-dual-polarization synthetic aperture radar (DualSAR) and design a polarimetric decomposition attention module to introduce the polarimetric parameters of LR-PolSAR images to maintain polarimetric information. Besides, we use the differential information between LR-PolSAR and HR-DualSAR to guide spatial resolution reconstruction. The loss function based on the$L_{1} $norm is used to constrain the network training process. The experimental results show the superiority of the proposed method over the existing methods in visual and quantitative evaluation. In addition, polarimetric decomposition experiments verify the effectiveness of the proposed method to maintain polarimetric information.

Deep learning-based synthetic aperture radar (SAR) image change detection has recently achieved remarkable success due to its great potential for extracting abstract features. However, the existing methods still have room for improvement in dealing with the speckle of SAR images. In this letter, a deep spatial–temporal gray-level co-occurrence aware convolutional neural network (STGCNet) is proposed, which can effectively mine the spatial–temporal information of the bitemporal images and obtain the speckle-robust results by introducing the 3-D gray-level co-occurrence matrix (3-D-GLCM) as auxiliary feature. Specifically, representative features are extracted from original image pairs and their corresponding 3-D-GLCM through two-stream network, followed by an adaptive fusion module to balance the contribution of each branch. Then, the final binary change detection results are obtained by a fully connected layer. The training process relies on reliable labels generated by unsupervised models rather than manually annotated data, and therefore, the proposed STGCNet is practical in reality. Experiments on synthesized and real SAR data sets demonstrate the robustness and competitiveness of the proposed method compared with the state-of-the-art algorithms.

Multispectral images play a crucial role in environmental monitoring or ecological analysis for their large scope, quick acquisition, and big data. With the rapid development of technology and increasing demand, very high-resolution multispectral images have attracted a lot of attention these days. However, due to sensor equipment and the imaging environment, the spatial resolution of multispectral images is always restricted. With the help of panchromatic images, pan-sharpening is a very important technique to enhance the spatial details of multispectral images. In this study, we proposed a knowledge optimization-driven pan-sharpening network with normalizer-free group ResNet prior, called PNXnet, which is unfolded from a physical knowledge optimization-driven variational model. We solved the memory overhead brought by the traditional ResNet relying on batch normalization. Results on four sensors show that high quantitative indexes and natural visual effects have verified the reliability of PNXnet. Focusing on the NIR band where spatial details are hard to be injected, we compared the Normalized Difference Vegetation Index (NDVI) generated from the fused results, the estimated NDVI shows a high consistency to the ground truth with R2above 0.91. Besides, we also compared the model generation. Furthermore, low model complexity and quicker computational speed make the daily application of PNXnet possible.

Fine particulate matter (PM2.5) pollution can cause serious public health problems worldwide. A novel geographically and temporally weighted neural network constrained by global training (GC-GTWNN) is proposed in this article for the remote sensing estimation of surface PM2.5. The global neural network (NN) is trained to learn the overall effect of the influencing variables on surface PM2.5, and the local geographically and temporally weighted NN (GTWNN) addresses the spatiotemporal heterogeneity of the relationship between PM2.5and the influencing variables. Specifically, a global NN is trained with all samples collected from the entire study domain and period. Then, initialized with the global NN, the GTWNN models are built for each location and time and fine-tuned via spatiotemporally localized samples. Meanwhile, the geographically weighted loss function is designed for GTWNN. The proposed GC-GTWNN modeling is tested with a case study across China, which integrates satellite aerosol optical depth, surface PM2.5measurements, and auxiliary variables. Cross-validation results indicate that a remarkable improvement is observed from the global NN to GC-GTWNN modeling ($R^{2}$value increasing from 0.49 to 0.80), and GC-GTWNN modeling also notably outperforms the conventionally popular PM2.5estimation models.

The existing nonblind cloud and cloud shadow (cloud/shadow) removal methods for remote sensing (RS) images are based on the assumption that cloud/shadow masks are accurately given. Since the masks are usually manually labeled or detected by cloud detection methods, whose accuracy cannot be well guaranteed, the cloud/shadow removal effect may be affected. In this article, we suggest a robust thick cloud/shadow removal (RTCR) method that meets the problem with an inaccurate mask. To faithfully reconstruct the multitemporal information, a coupled tensor factorization is used to explore the relationship between the abundances of the multitemporal images in the same scene. Moreover, an efficient algorithm is developed to solve the proposed model based on the augmented Lagrange multiplier method. The experimental results under accurate masks and inaccurate masks demonstrate its robustness and superiority for thick cloud/shadow removal.

The data fusion technology aims to aggregate the characteristics of different data and to obtain products with multiple data advantages. To solve the problem of reduced resolution of polarimetric synthetic aperture radar (PolSAR) images due to system limitations, we propose a fully PolSAR images and single-polarization synthetic aperture radar (SinSAR) images fusion network to generate high-resolution PolSAR (HR-PolSAR) images. To take advantage of the polarimetric information of the low-resolution PolSAR (LR-PolSAR) images and the spatial information of the high-resolution single-polarization SAR (HR-SinSAR) images, we propose a fusion framework for joint LR-PolSAR images and HR-SinSAR images and design a cross-attention mechanism to extract features from the joint input data. Besides, based on the physical imaging mechanism, we designed the PolSAR polarimetric loss functions for constrained network training. The experimental results confirm the superiority of the fusion network over traditional algorithms. The average peak signal-to-noise ratio (PSNR) is increased by more than 3.6 dB, and the average mean absolute error (MAE) is reduced to less than 0.07. Experiments on polarimetric decomposition and polarimetric signature show that it maintains polarimetric information well.

The normalized difference vegetation index (NDVI) can reflect the plant life cycle of growth and senescence and has become a widely used tool for many applications related to phenology, ecology, and environment. However, unwanted disturbance from cloud, snow, and other atmospheric effects greatly lowers the NDVI quality and hinders its further application. In this article, differing from the previous research attempting to approach the upper NDVI envelope by local adjustment or threshold-related iteration, a novel one-step global variational reconstruction (OGVR) method for NDVI time series is proposed via joint modeling of the gradual vegetation change and negatively biased atmospheric contamination. Two versions of the proposed method are designed for processing NDVI data with or without auxiliary flag information. Long-term and global-scale Advanced Very High Resolution Radiometer (AVHRR) global inventory monitoring and modeling system (GIMMS) data were applied in simulated and real-data experiments to verify the proposed method. The results show that the proposed method can successfully estimate the natural vegetation change from seriously contaminated NDVI time series and can conquer the problem of continuous low-value gaps. The qualitative and quantitative comparisons with five other widely used methods indicate that the proposed method has significant advantages in terms of both effectiveness and stability.

Hyperspectral images (HSIs) are generally distorted by various types of damage and degradation due to limited imaging conditions. Hence, noise reduction is an essential process before HSI interpretations and applications. In this paper, a novel local-global feature-aware transformer based residual network (FATR) is proposed for hyperspectral image denoising. First, a spatial-spectral feature extraction module is built to extract spatial and spectral shallow features simultaneously. Second, these spatial-spectral features are forwarded to the deep feature extraction module, which contains several local-global feature-aware transformer blocks, where contextual information as well as local and global information can be further aggregated by multiscale windows transformer layers. Finally, in the reconstruction module, different hierarchical features from branches of two modules are merged into the final restoration to recover clean HSIs. Extensive experiments on both synthetic and real-world data demonstrate that the model has a better ability to restore HSIs in terms of evaluation metrics and visual assessments.

With the development of video satellites, multimoving object tracking in satellite video is possible and has become a new challenging task. The difficulties are mainly caused by the characteristics of satellite videos: 1) small objects; 2) low contrast between objects and background; and 3) background in a state of continuous motion. These characteristics make it difficult for the advanced multiobject tracking algorithms in the natural video to give full play to their advantages, resulting in vast false alarms, missed objects, ID switches, and low-confidence bounding boxes. To tackle these problems, a novel multimoving object tracking method considering slow features (SFs) and motion features has been proposed in this research, named SF and motion feature-guided multiobject tracking (SFMFMOT), which realizes the continuous tracking of moving vehicles in satellite videos. A nonmaximum suppression (NMS) module guided by bounding box proposals based on SFs is designed to assist the object detection part by utilizing the sensitivity of SF analysis to the changed pixels. While removing a large number of static false alarms and supplementing missed objects, it improves the recall rate by increasing the confidence score of the correctly detected object bounding boxes. In order to improve the tracking performance, a set of optimization strategies based on motion features and time accumulation information are proposed to smooth the trajectory, remove static false alarms, and duplicate bounding boxes. The proposed method is evaluated in three satellite videos and its superiority is demonstrated.

Enhancement of hyperspectral image (HSI) resolution is significant for better application in practice. In this article, a dual U-Net (D-UNet) is proposed to improve the spatial resolution of HSI. The whole network contains two parts. One is the detail extraction network, whose network architecture is encoder–decoder and mainly extracts various spatial features from multispectral images (MSIs). Another is the spatio-spectral fusion network (SSFN), which aims at injecting the features from the detail extraction network into HSI for better reconstruction. Furthermore, in the primary stage of the whole network, a novel multiscale spatio-spectral attention module (MSSAM) is utilized to pay more attention to important features at different scales. Considering the complex ground scenes, the features of different scale and depth are continually extracted and fused in the whole network. The experimental results show that the proposed method is more effective compared with the state-of-the-art methods.

As a new earth observation tool, satellite video has been widely used in remote-sensing field for dynamic analysis. Video super-resolution (VSR) technique has thus attracted increasing attention due to its improvement to spatial resolution of satellite video. However, the difficulty of remote-sensing image alignment and the low efficiency of spatial–temporal information fusion make poor generalization of the conventional VSR methods applied to satellite videos. In this article, a novel fusion strategy of temporal grouping projection and an accurate alignment module are proposed for satellite VSR. First, we propose a deformable convolution alignment module with a multiscale residual block to alleviate the alignment difficulties caused by scarce motion and various scales of moving objects in remote-sensing images. Second, a temporal grouping projection fusion strategy is proposed, which can reduce the complexity of projection and make the spatial features of reference frames play a continuous guiding role in spatial–temporal information fusion. Finally, a temporal attention module is designed to adaptively learn the different contributions of temporal information extracted from each group. Extensive experiments on Jilin-1 satellite video demonstrate that our method is superior to current state-of-the-art VSR methods.

Hyperspectral imagery (HSI) is one of the emerging tools to explore the physical properties and chemical composition of the lunar surface. Moon mineralogy mapper (M3) is the most widely used lunar HSI data set with the widest coverage and the excellent resolution; however, dense and nonperiodic stripes distributed across all bands in M3images hinder visual interpretation as well as their use in subsequent applications. In this article, a fast destriping approach for M3is proposed using the Hodrick–Prescott decomposition embedded in the low-rank framework (LRHP) to overcome this limitation. The integration of a statistical filter and variational model tackles the problem stemming from a lack of the correct residual information when certain pixels are corrupted in every band, thereby restoring severely degraded hyperspectral images (HSIs). Simulated and real experiments conducted on typical regions on the Moon with various levels of corruption demonstrate that the proposed LRHP rapidly achieves favorable performance against state-of-the-art approaches. Also, expanding tests on interference imaging spectrometer (IIM) data of Chang’E-1 and commonly used terrestrial remote sensing images show that LRHP has good generalization capability. Moreover, the integrated band depth (IBD) maps further verify the necessity of destriping and the high spectral fidelity of LRHP that benefits further applications.

Model-driven methods and data-driven methods have been widely developed for hyperspectral image (HSI) denoising. However, there are pros and cons in both model-driven and data-driven methods. To address this issue, we develop a self-supervised HSI denoising method via integrating model-driven with data-driven strategy. The proposed framework simultaneously cooperates the spectral low-rankness prior and deep spatial prior (SLRP-DSP) for HSI self-supervised denoising. SLRP-DSP introduces the Tucker factorization via orthogonal basis and reduced factor, to capture the global spectral low-rankness prior in HSI. Besides, SLRP-DSP adopts a self-supervised way to learn the deep spatial prior. The proposed method doesn't need a large number of clean HSIs as the label samples. Through the self-supervised learning, SLRP-DSP can adaptively adjust the deep spatial prior from self-spatial information for reduced spatial factor denoising. An alternating iterative optimization framework is developed to exploit the internal low-rankness prior of third-order tensors and the spatial feature extraction capacity of convolutional neural network. Compared with both existing model-driven methods and data-driven methods, experimental results manifest that the proposed SLRP-DSP outperforms on mixed noise removal in different noisy HSIs.

Hyperspectral images (HSIs) are crucial for many research works. Spectral super-resolution (SSR) is a method used to obtain high-spatial-resolution (HR) HSIs from HR multispectral images. Traditional SSR methods include model-driven algorithms and deep learning. By unfolding a variational method, this article proposes an optimization-driven convolutional neural network (CNN) with a deep spatial-spectral prior, resulting in physically interpretable networks. Unlike the fully data-driven CNN, auxiliary spectral response function (SRF) is utilized to guide CNNs to group the bands with spectral relevance. In addition, the channel attention module (CAM) and the reformulated spectral angle mapper loss function are applied to achieve an effective reconstruction model. Finally, experiments on two types of data sets, including natural and remote sensing images, demonstrate the spectral enhancement effect of the proposed method, and also, the classification results on the remote sensing data set verified the validity of the information enhanced by the proposed method.

Three-dimensional (3D) convolution is well-suited for volumetric data exploration, and therefore it has great potential in spatial-spectral feature learning to promote hyperspectral image super-resolution (HSI SR). However, 3D convolution is computationally expensive, and this is especially true when it operates on the high spectral dimensionality. In this paper, we design the enhanced 3D (E3D) convolution, an efficient form of spatial-spectral convolution. The standard 3D convolution is factorized into sequential spatial and spectral components. And the novel lightweight spatial and spectral squeeze-and-excitation modules are incorporated to corresponding components, respectively. As such, E3D convolution can largely reduce the computational complexity and extract effective spatial-spectral features with the holistic information. We further construct a fully 3D convolutional network (E3DN) with the proposed E3D convolution. The additional global residual learning and share-source skip connections can achieve spectral mapping and facilitate feature propagation. The simulated and real experiments demonstrate the accuracy and performance advantages of E3DN.

Deep learning-based methods have shown superior performance in VSR tasks. However, satellite video frames are characterized by large width, low resolution, and lack of features. Consequently, the conventional VSR method is not suitable for satellite video. In this paper, a recurrent refinement network is proposed. Considering that the vast majority of remote sensing images belong to the static background, a single-image SR (SISR) method is first used to obtain high-resolution features for a specific target frame. To further complement the missing details, the network learns the complementary information enhanced by an Encoder-Decoder structure from adjacent frames to refine the results of SISR. To measure the contribution of different adjacent frames to the recovery of the target frame, a temporal attention mechanism is introduced in the final fusion stage. The experiment on the video data of Jilin-1 demonstrates the effectiveness of our method.

It is very significant to enhance the spatial resolution of hyperspectral images for more accurate image interpretation. In this paper, we propose an innovative fusion method by the enhanced residual dense network to better extract the spatio-spectral features joint with GRU. The enhanced residual dense blocks (ERDB) contains a modified spatial and spectral attention module. All attention coefficients are calculated based on original images. Then, the idea of gate recurrent unit (GRU) is applied to integrate the useful information from results of all ERDBs followed by a convolution for reconstruction. Finally, a skip connection is added to further maintain the spectrum. The effectiveness of this method can be seen from the experimental results of simulation experiments.

In this study, we combine both the deep prior with low-rank tensor completion (DP-LRTC) for thick cloud removal in Sentinel-2 time-series images. On the one hand, DP-LRTC utilizes the low-rank property of multitemporal images via 3-order tensor completion. On the other hand, DP-LRTC employs the 3D spatiotemporal feature expression ability by deep learning. Through integrating both model-driven with data-driven strategy, the proposed method can effectively removal thick cloud in Sentinel-2 time-series images.

Although unprecedented success has been achieved in convolutional neural network (CNN)-based super-resolution (SR) for natural images, hyperspectral image (HSI) SR without auxiliary high-resolution images remains a challenging task due to the high spectral dimensionality, where learning effective spatial and spectral representations is of great importance. In this article, we introduce a novel CNN-based HSI SR method, termed spectral grouping and attention-driven residual dense network (SGARDN) to facilitate the modeling of all spectral bands and focus on the exploration of spatial-spectral features. Considering the block characteristic of HSI, we employ group convolutions in and between groups composed of highly similar spectral bands at early stages to extract informative spatial features and avoid spectral disorder caused by normal convolution. To exploit spectral prior, a new spectral attention mechanism constructed by covariance statistics of features is designed to adaptively recalibrate features. We adapt the spectral attention for group convolutions to rescale grouping features with holistic spectral information. These two sequential operations called spectral grouping and integration module aim to extract effective shallow spatial-spectral features that are reused in the following layers. On the other hand, the residual dense block can better deal with spatial-spectral features by experimental comparison and hence is combined with the spectral attention to form a new basic building block for powerful feature expression and spectral correlation learning. The experimental results on synthesized and real-scenario HSIs demonstrate the feasibility and superiority of the proposed method over other state-of-the-art methods.

Synthetic aperture radar (SAR) images are inherently affected by speckle noise, for which deep learning-based methods have shown good potential. However, the deep learning-based methods proposed until now directly map low-quality images to high-quality images, and they are unable to characterize the priors for all the kinds of speckle images. The variational method is a classic model optimization approach that establishes the relationship between the clean and noisy images from the perspective of a probability distribution. Therefore, in this article, we propose the recursive deep convolutional neural network (CNN) prior model for SAR image despeckling (SAR-RDCP). First, the data-fitting term and regularization term of the SAR variational model are decoupled into two subproblems, i.e., a data-fitting block and a deep CNN prior block. The gradient descent algorithm is then used to solve the data-fitting block, and a predenoising residual channel attention network based on dilated convolution is used for the deep CNN prior block, which combines an end-to-end iterative optimization training. In the experiments undertaken in this study, the proposed model was compared with several state-of-the-art despeckling methods, obtaining better results in both the quantitative and qualitative evaluations.

In this paper, five spectral superresolution (SSR) algorithms are compared to verify the availability of SSR results as input data in classification. To enhance the spectral resolution, SSR algorithms are proposed to increase the channel number of multispectral images, which can be divided into model-driven and data-driven methods. To combine the advantage of these two types of algorithms, we proposed an optimization-inspired convolutional neural network (OCNN) by unfolding a traditional variational model. The proposed method combines data-driven training with model-driven optimization together to enhance the spectral resolution of high-resolution (HR) multispectral images (MSIs) to obtain HR hyperspectral images (HSIs). Experiments in both SSR and classification are made to show the proposed method is of efficiency and superiority.

Tropospheric Monitoring Instrument (TROPOMI) carried onboard the Sentinel-5 Precursor (S5P) satellite could provide a daily global product of carbon monoxide (CO) total column at a high spatial resolution of 7*7 km2. However, data gaps exist in the TROPOMI CO product, which likely blocks the researches on the atmospheric CO. Therefore, a fusing method based on XGBoost is purposed in this study to recover the missing data in the TROPOMI CO product. The results show that the R and RMSE for the sample-based cross validation are 0.952 and 0.00303, respectively. It's believed that the proposed method could accurately predict the TROPOMI CO total column. Meanwhile, the spatial patterns of the daily predicted CO total column are similar to those of the TROPOMI. For temporal variations, the proposed method is able to mitigate the rapid fluctuations of the region-averaged TROPOMI CO total column in time series.

In this paper, we present a novel hyperspectral image (HSI) denoising method especially for mixed noise removal. The proposed method combines both data-driven with model-driven strategy via a deep spatio-spectral variational structure. The mixed noise estimation and removal are collaboratively derived through fusing the Bayesian spatio-spectral posterior and deep learning model. The framework can both utilize the logicality of traditional model-driven methods, and the high efficiency of data-driven methods for parameters optimizing. Simulated and actual experiments demonstrate that the presented method outperforms other existing methods for HSI mixed noise removal, on both reconstructing effects and time-consuming.

Compared with traditional remote sensing images, satellite remote sensing video contains more useful information and can capture continuous dynamic video. Recently, many deep-learning based methods have been proposed for video super resolution. However, these methods tend to ignore the structural information and characteristics for video satellite imagery such as small ground targets, a wide range of scales and weak textures. To this end, this paper proposes a single-and-multi-frame ensembled framework called SMFE for remote sensing videos super-resolution. The SMFE framework combines a non-local based single image super resolution (SISR) network and a state-of-the-arts multi-frame super resolution (MFSR) network EDVR. Experiments have been performed to demonstrate the effectiveness of the proposed method on Jilin-1.

The lunar hyperspectral remote sensing is one of the most important means to understand the physical properties and chemical constituents of lunar surface materials. Moon Mineral Mapper (M3) is currently the only hyperspectral image (HSI) data of Moon. However, due to the limitations of sensor manufacture and the impact of complex extraterrestrial environment, there are serious stripes on the M3 images, which do harm to subsequent identifications and analysis. In this paper, an effective destriping algorithm for lunar HSIs based on the intrinsic characteristics of the stripes on M3 is proposed. Experimental results demonstrate that our method shows an improvement in terms of visual perception and spectral fidelity.

Remote sensing image super-resolution has always been a major research focus, and many deep-learning-based algorithms have been proposed in recent years. However, since the structure of remote sensing images tends to be much more complex than that of natural images, several difficulties still remain for remote sensing images super-resolution. First, it is difficult to depict the nonlinear mapping between high-resolution (HR) and low-resolution (LR) images of different scenes with the same model. Second, the wide range of scales within the ground objects in remote sensing images makes it difficult for single-scale convolution to effectively extract features of various scales. To address the above-mentioned issues, we propose a multiscale attention network (MSAN) to extract the multilevel features of remote sensing images. The basic component of MSAN is the multiscale activation feature fusion block (MAFB). In addition, a scene-adaptive super-resolution strategy for remote sensing images is employed to more accurately describe the structural characteristics of different scenes. The experiments undertaken on several data sets confirm that the proposed algorithm outperforms the other state-of-the-art algorithms, in both evaluation indices and visual results.

In this work, a progressively spatio-temporal patch group learning framework for cloud and shadow removal in Sentinel-2 data is proposed. Through sorting the spatial and corresponding multi-temporal patches with masks as the patch group fashion, a spatiotemporal patch group recovering model is developed using a global-local deep CNN. Finally, all the ergodic patches are weighted aggregated with integrity measure, then updated spatial data and its mask are regenerated through progressive iteration. Two experiments have been performed to demonstrate the effectiveness of the proposed method on Sentienl-2 MSI data, with single/multiple temporal imageries in small and largescale scenarios.

In this paper, a new pansharpening method with residual convolutional neural network (RCNN)) is proposed. The proposed method utilizes a novel end-to-end CNN, which maps the differential information between the high spatial resolution panchromatic image (HR-PAN) and the low spatial resolution multispectral image (LR-MS) to the differential information between the HR-PAN image and the high spatial resolution multispectral image (HR-MS). Unlike the CNN-based pansharpening methods in other literatures, the proposed method makes full use of the spatial information in the HR-PAN image, and simultaneously preserve the spectral information of the MS image. Experimental results at both reduced resolution and full resolution demonstrate the superior performance of the proposed method comparing to state-of-the-art pansharpening methods in both quantitative and visual assessments.

Vegetation water content (VWC) is recognized as an important parameter in vegetation growth study. Recently, the ground-based GNSS-R method is emerging in monitoring VWC owing to its high accuracy. However, the small footprint and sparse distribution hinder its application. Therefore, we propose a method to improve the spatial resolution of GNSS-R VWC products by downscaling with other products highly correlated with VWC, using random forest (RF). Satisfactory downscaling results with cross-validation R values of 0.83 and RMSE of 0.025 were obtained. VWC images with a 500-m spatial resolution were then acquired, which is consistent with the distribution of NDVI and GPP, further indicating the accuracy of the downscaling results.

In order to solve the problem of full-polarimetric SAR image degradation, this paper proposes a full-polarimetric SAR image super-resolution reconstruction method combined with a convolutional neural network and residual compensation. Through the advantages of the deep convolutional neural network for nonlinear model fitting, this paper performs super-resolution reconstruction on low-resolution full-polarimetric SAR images, and then applies residual compensation to network reconstruction results, using low-resolution image information to the network. The super-resolution reconstruction results are corrected to obtain a high-resolution full-polarimetric SAR image. Compared with the traditional full-polarimetric SAR image super-resolution reconstruction method, the proposed method shows excellent results in both visual and quantitative evaluation indicators, especially the reconstruction of detailed information.

Deriving accurate snow depth is of great importance since snow cover is an informative indicator of climate change. The objective of this study is to develop a snow-depth retrieval algorithm based on a deep learning approach by fusing passive microwave remote sensing brightness temperature, station observations and GNSS-R snow-depth product to improve the accuracy of snow-depth retrieval. The results show that DBN performs the best compared with three alternative algorithms.

In this study, the performance of the MODIS C6 multiangle implementation of atmospheric correction (MAIAC) 1-km aerosol optical depth (AOD) products in China during 2008-2016 are validated using 17 AERONET sites. The results show that the correlation coefficient (R) for MAIAC in C6 is 0.942 and the RMSE is 0.2. Meanwhile, the fraction above the EE is shown as 49.85%, indicating that the MAIAC retrievals will integrally overestimate AOD values in China. Especially, significant overestimation of MAIAC is found at vegetation sites with high elevation and in areas round water. The scale of high-resolution AOD distribution is fine while an obvious boundary caused by aerosol models is also observed.

This study attempts to develop a novel solution for the estimation of regional surface soil moisture (SSM) using a machine learning model trained on in situ measurement target data. Specifically, the generalized regression neural network (GRNN) is employed to establish the relationship between in-situ measurements from Sparse Network Stations (SNSs) in the continental U.S. and passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite for April 2015 to March 2018. More importantly, to address the scale mismatch issue resulting from the small spatial support of in situ measurements, we turn to the extended triple collocation technique whereby individual SNSs' reliability at the SMAP coarse footprint is determined before fed into GRNN. The cross-validation results show that the GRNN model trained on reliable SNSs obtains a fairly good performance, with out-of-sample cross-validated R and unbiased RMSE values of 0.92 and 0.043 cm3cm-3, respectively. Moreover, the comparison in space shows that the spatial patterns of GRNN retrievals is the most consistent with in situ measurements than both the SMAPL3SMP and the ERA-Interim SSM data. Furthermore, the GRNN-estimated SSM time series over stations agrees much better with in-situ measurements than the official SMAP passive SSM product. All these results indicate that the statistical GRNN modeling has shown great potential in estimating reliable regional SSM climate records using in-situ measurements as training references.

Enhancing the spatial resolution of hyperspectral (HS) images by fusing with higher spatial resolution multispectral (MS) data is of significance for applications. However, due to the narrow bandwidth, HS images (HSIs) are vulnerable to various types of noise, such as Gaussian noise and stripes, which can severely affect the fusion performance. This paper focuses on antinoise HS and MS image fusion to enhance the spatial details and suppress the noise. By analysis of the intrinsic structure and noise properties, we formulate this problem as the minimization of an objective function. Under the optimization framework, small multilinear ranks in tensor are first used to identify the intrinsic structures of the clean HSI part. Then, considering the high spectral correlation, it is assumed that any bands can be represented by the combination of certain adjacent bands. The difference between one band and its corresponding combination can be used to preserve the spatio-spectral consistency and characterize the distribution of sparse noise (such as stripe noise), based on the variational properties along two directions. The alternating direction method of multipliers (ADMM) is applied to solve and accelerate the model optimization. Experiments with both simulated- and real-data demonstrate the effectiveness of the proposed model and its robustness to the noise, in terms of both qualitative and quantitative perspectives.

The optical remote sensing images not only have to make a fundamental tradeoff between the spatial and spectral resolutions, but also are inevitable to be polluted by the clouds; however, the existing pansharpening methods mainly focus on the resolution enhancement of the optical remote sensing images without cloud contamination. How to fuse the cloud-contaminated images to achieve the joint resolution enhancement and cloud removal is a promising and challenging work. In this paper, a pansharpening method for the challenging cloud-contaminated very high-resolution remote sensing images is proposed. Furthermore, the cloud-contaminated conditions for the practical observations with all the thick clouds, the thin clouds, the haze, and the cloud shadows are comprehensively considered. In the proposed methods, a two-step fusion framework based on multisource and multitemporal observations is presented: 1) the thin clouds, the haze, and the light cloud shadows are proposed to be first jointly removed and 2) a variational-based integrated fusion model is then proposed to achieve the joint resolution enhancement and missing information reconstruction for the thick clouds and dark cloud shadows. Through the proposed fusion method, a promising cloud-free fused image with both high spatial and high spectral resolutions can be obtained. To comprehensively test and verify the proposed method, the experiments were implemented based on both the cloud-free and cloud-contaminated images, and a number of different remote sensing satellites including the IKONOS, the QuickBird, the Jilin (JL)-1, and the Deimos-2 images were utilized. The experimental results confirm the effectiveness of the proposed method.

In the field of spatial–spectral fusion, the variational model-based methods and the deep learning (DL)-based methods are state-of-the-art approaches. This paper presents a fusion method that combines the deep neural network with a variational model for the most common case of spatial–spectral fusion: panchromatic (PAN)/multispectral (MS) fusion. Specifically, a deep residual convolutional neural network (CNN) is first trained to learn the gradient features of the high spatial resolution multispectral image (HR-MS). The image observation variational models are then formulated to describe the relationships of the ideal fused image, the observed low spatial resolution multispectral image (LR-MS) image, and the gradient priors learned before. Then, fusion result can then be obtained by solving the fusion variational model. Both quantitative and visual assessments on high-quality images from various sources demonstrate that the proposed fusion method is superior to all the mainstream algorithms included in the comparison, in terms of overall fusion accuracy.

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications. In this paper, a novel deep learning-based method for this task is proposed, by learning a nonlinear end-to-end mapping between the noisy and clean HSIs with a combined spatial-spectral deep convolutional neural network (HSID-CNN). Both the spatial and spectral information are simultaneously assigned to the proposed network. In addition, multiscale feature extraction and multilevel feature representation are, respectively, employed to capture both the multiscale spatial-spectral feature and fuse different feature representations for the final restoration. The simulated and real-data experiments demonstrate that the proposed HSID-CNN outperforms many of the mainstream methods in both the quantitative evaluation indexes, visual effects, and HSI classification accuracy.

The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSI applications. In this paper, the spatial-spectral gradient network (SSGN) is presented for mixed noise removal in HSIs. The proposed method employs a spatial-spectral gradient learning strategy, in consideration of the unique spatial structure directionality of sparse noise and spectral differences with additional complementary information for effectively extracting intrinsic and deep features of HSIs. Based on a fully cascaded multiscale convolutional network, SSGN can simultaneously deal with different types of noise in different HSIs or spectra by the use of the same model. The simulated and real-data experiments undertaken in this study confirmed that the proposed SSGN outperforms at mixed noise removal compared with the other state-of-the-art HSI denoising algorithms, in evaluation indices, visual assessments, and time consumption.

Satellite remote sensing is a promising approach for the estimation of ground-level PM2.5. In this paper, a deep learning framework for satellite-based PM2.5 estimation is presented. Taking advantage of multi-layer learning and layer-by-layer pre-training, deep learning has the great potential to mine nonlinear relationship between PM2.5 and satellite observations. Firstly, the presented deep learning framework can be employed to estimate ground PM2.5 using satellite-derived aerosol optical depth (AOD). Secondly, the AOD products are retrieved from satellite top-of-atmosphere (TOA) reflectance. The deep learning framework can further be adopted to estimate ground PM2.5 directly from satellite TOA reflectance. The presented framework was tested for AOD-based PM2.5 estimation in China and reflectance-based PM2.5 estimation across Wuhan Metropolitan Area, respectively. The results show that the deep learning framework achieves an outstanding performance for both AOD-based and reflectance-based PM2.5 estimation. This study provides an effective way for the satellite-based estimation of PM2.5.

In this paper, a unified spatial-temporal-spectral framework of missing information reconstruction in remote sensing images is proposed. Based on an end-to-end non-linear mapping structure, the proposed method employs a unified deep convolutional neural network combined with joint spatial-temporal-spectral supplementary information. It should be noted that the proposed model can use multi-source data (spatial, spectral, and temporal) as the input of the unified framework. The results of real-data experiments demonstrate that the proposed model exhibits high effectiveness in missing information reconstruction tasks like dead lines in Aqua MODIS band 6, Landsat ETM+ SLC-off and thick cloud removal.

Striping effects are a common phenomenon in remote-sensing imaging systems, and they can exhibit considerable differences between different sensors. Such artifacts can greatly degrade the quality of the measured data and further limit the subsequent applications in higher level remote-sensing products. Although a lot of destriping methods have been proposed to date, a few of them are robust to different types of stripes. In this paper, we conduct a thorough feature analysis of stripe noise from a novel perspective. With regard to the problem of striping diversity and complexity, we propose a universal destriping framework. In the proposed destriping procedure, a 1-D variational method is first designed and utilized to estimate the statistical feature-based guidance. The guidance information is then incorporated into 2-D optimization to control the image estimation for a reliable and clean output. The iteratively reweighted least-squares method and alternating direction method of multipliers are exploited in the proposed approach to solve the minimization problems. Experiments under various cases of simulated and real stripes confirm the effectiveness and robustness of the proposed model in terms of the qualitative and quantitative comparisons with other approaches.

Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.e., the data usability is greatly reduced. In this paper, a novel method of missing information reconstruction in remote sensing images is proposed. The unified spatial-temporal-spectral framework based on a deep convolutional neural network (CNN) employs a unified deep CNN combined with spatial-temporal-spectral supplementary information. In addition, to address the fact that most methods can only deal with a single missing information reconstruction task, the proposed approach can solve three typical missing information reconstruction tasks: (1) dead lines in Aqua Moderate Resolution Imaging Spectroradiometer band 6; (2) the Landsat Enhanced Thematic Mapper Plus scan line corrector-off problem; and (3) thick cloud removal. It should be noted that the proposed model can use multisource data (spatial, spectral, and temporal) as the input of the unified framework. The results of both simulated and real-data experiments demonstrate that the proposed model exhibits high effectiveness in the three missing information reconstruction tasks listed above.

Pan-sharpening is a fundamental and significant task in the field of remote sensed imagery fusion, which demands fusion of panchromatic and multi-spectral images with the rich information accurately preserved in both spatial and spectral domains. In this paper, to overcome the drawbacks of traditional pan-sharpening methodologies, we employed the advanced concept of deep learning to propose a Multi-Scale-and-Depth Convolutional Neural Network (MSDCNN) as an end-to-end pan-sharpening model. By the results of a large number of quantitative and visual assessments, the qualities of images fused by the proposed network have been confirmed superior to compared state-of-the-art methods.

Missing data reconstruction (e.g., dead pixel repair and cloud removing) in remote sensing images is a very important problem for the subsequent image analysis. It is well-known that missing data reconstruction is an ill-posed problem. In remote sensing images, there is a strong correlation in spectral frequencies or in temporal frames, and also there are a lot of self-similarity patterns in spatial domain. We can make use of these properties to derive low rank matrices according to their spectral, temporal and spatial dimensions. In this paper, we propose a tensor completion model based on these low rank matrices to deal with missing data reconstruction problem. We also present a weighting method for spectral, temporal and spatial dimensions and for their distribution of singular values. Our experimental results demonstrate that the weighting method can recover remote images very well. In particular, we show the effectiveness of the proposed method for both simulated and real data sets, and the performance of the proposed in terms of visual and quantitative measures is better than those of the other testing methods.

In the field of multispectral (MS) and panchromatic image fusion (pansharpening), the impressive effectiveness of deep neural networks has recently been employed to overcome the drawbacks of the traditional linear models and boost the fusion accuracy. However, the existing methods are mainly based on simple and flat networks with relatively shallow architectures, which severely limits their performance. In this letter, the concept of residual learning is introduced to form a very deep convolutional neural network to make the full use of the high nonlinearity of the deep learning models. Through both quantitative and visual assessments on a large number of high-quality MS images from various sources, it is confirmed that the proposed model is superior to all the mainstream algorithms included in the comparison, and achieves the highest spatial-spectral unified accuracy.

Missing information, such as dead pixel values and cloud effects, is very common image quality degradation problems in remote sensing. Missing information can reduce the accuracy of the subsequent image processing, in applications such as classification, unmixing, and target detection, and even the quantitative retrieval process. The main aim of this paper is to study an adaptive weighted tensor completion (AWTC) method for the recovery of remote sensing images with missing data. Our idea is to collectively make use of the spatial, spectral, and temporal information to build a new weighted tensor low-rank regularization model for recovering the missing data. In the model, the weights are determined adaptively by considering the contribution of the spatial, spectral, and temporal information in each dimension. Experimental results based on both simulated and real data sets are presented to verify that the proposed method can recover missing data, and its performance is found to be better than the other tested methods. In the simulated experiments, the peak signal-to-noise ratio is improved by more than 3 dB, compared with the original tensor completion model. In the real data experiments, the proposed AWTC model can better recover the dead line problem in Aqua Moderate Resolution Imaging Spectroradiometer band 6 and the scan-line corrector-off problem in enhanced thematic mapper plus images, with the smallest spectral distortion.

In this paper, we introduced a deep learning model: Convolutional neural network(CNN) from the field of natural image classification and restoration, to solve general quality improving tasks for remote sensing images, including super-resolution, denoising and haze removal. To take advantage of the content similarity among aerial images and the learning ability of deep learning models, we proposed the idea of training CNN on datasets collected from aerial images with specific degenerating factors, then apply the model to matched tasks. Experiments showed that our network achieved superior performance in quantified results, and visually reconstructed a satisfying majority of missing details from low-quality observations.

Due to the limitation of hyperspectral sensors and optical imaging systems, there are several irreconcilable conflicts between high spatial resolution and high spectral resolution of hyperspectral images (HSIs). Therefore, HSI super-resolution (SR) is regarded as an important preprocessing task for subsequent applications. In this letter, we use sparse representation to analyze the spectral and spatial feature of HSIs. Considering the sparse characteristic of spectral unmixing and high pattern repeatability of spatial-spectral blocks, we proposed a novel HSI SR framework utilizing spectral mixture analysis and spatial-spectral group sparsity. By simultaneously combining the sparsity and the nonlocal self-similarity of the images in the spatial and spectral domains, the method not only maintains the spectral consistency but also produces plenty of image details. Experiments on three hyperspectral data sets confirm that the proposed method is robust to noise and achieves better results than traditional methods.

In the commonly employed regularization models of image restoration and super-resolution (SR), the norm determination is often challenging. This paper proposes a method to adaptively determine the optimal norms for both fidelity term and regularization term in the (SR) restoration model. Inspired by a generalized likelihood ratio test, a piecewise function is proposed to solve the norm of the fidelity term. This function can find the stable norm value in a certain number of iterations, regardless of whether the noise type is Gaussian, impulse, or mixed. For the regularization norm, the main advantage of the proposed method is that it is locally adaptive. Specifically, it assigns different norms for different pixel locations, according to the local activity measured by a structure tensor metric. The proposed method was tested using different types of images. The experimental results and error analyses verify the efficacy of the method.

Hyperspectral images (HSIs) are inevitably corrupted by mixture noise during their acquisition process, in which various kinds of noise, e.g., Gaussian noise, impulse noise, dead lines, and stripes, may exist concurrently. In this paper, mixture noise removal is well illustrated by the task of recovering the low-rank and sparse components of a given matrix, which is constructed by stacking vectorized HSI patches from all the bands at the same position. Instead of applying a traditional nuclear norm, a nonconvex low-rank regularizer, i.e., weighted Schatten p-norm (WSN), is introduced to not only give better approximation to the original low-rank assumption but also to consider the importance of different rank components. The resulted nonconvex low-rank matrix approximation (LRMA) model falls into the applicable scope of an augmented Lagrangian method, and its WSN minimization subproblem can be efficiently solved by generalized iterated shrinkage algorithm. Moreover, the proposed model is integrated into an iterative regularization schema to produce final results, leading to a completed HSI restoration framework. Extensive experimental testing on simulated and real data shows, both qualitatively and quantitatively, that the proposed method has achieved highly competent objective performance compared with several state-of-the-art HSI restoration methods.

Hyperspectral image (HSI) denoising is a crucial preprocessing task that is used to improve the quality of images for object detection, classification, and other subsequent applications. It has been reported that noise can be effectively removed using the sparsity in the nonnoise part of the image. With the appreciable redundancy and correlation in HSIs, the denoising performance can be greatly improved if this redundancy and correlation is utilized efficiently in the denoising process. Inspired by this observation, a noise reduction method based on joint spectral-spatial distributed sparse representation is proposed for HSIs, which exploits the intraband structure and the interband correlation in the process of joint sparse representation and joint dictionary learning. In joint spectral-spatial sparse coding, the interband correlation is exploited to capture the similar structure and maintain the spectral continuity. The intraband structure is utilized to adaptively code the spatial structure differences of the different bands. Furthermore, using a joint dictionary learning algorithm, we obtain a dictionary that simultaneously describes the content of the different bands. Experiments on both synthetic and real hyperspectral data show that the proposed method can obtain better results than the other classic methods.

Remote sensing images are often contaminated by varying degrees of stripes, which severely affects the visual quality and subsequent application of the data. Unlike with conventional methods, we achieve the destriping by separating the stripe component based on a full analysis of the various stripe properties. Under an optimization framework, an ℓ0-norm-based regularization is used to characterize the global sparse distribution of the stripes. In addition, difference-based constraints are adopted to describe the local smoothness and discontinuity in the along-stripe and across-stripe directions, respectively. The alternating direction method of multipliers is applied to solve and accelerate the model optimization. Experiments with both simulated and real data demonstrate the effectiveness of the proposed model, in terms of both qualitative and quantitative perspectives.

In this paper, a unified framework for the spatio-temporal-spectral fusion of remote sensing images is proposed. The relationships between the observed images and the desired image are first established based on general image observation models. Maximum a posteriori (MAP) theory is then employed to formulate the unified fusion framework. The proposed method is able to fuse images from an arbitrary number of optical sensors with different spatial, temporal, and spectral resolutions. The experimental results verify the effectiveness of the proposed method.

The digital elevation model (DEM) is a significant digital representation of a terrain surface. Although a variety of DEM products are available, they often suffer from problems varying in spatial coverage, data resolution, and accuracy. However, the multi-source DEMs often contain supplementary information, which makes it possible to produce a higher-quality DEM through blending the multi-scale data. Inspired by super-resolution (SR) methods, we propose a regularized framework for the production of high-resolution (HR) DEM data with extended coverage. To deal with the registration error and the horizontal displacement among multi-scale measurements, robust data fidelity with weighted norm is employed to measure the conformance of the reconstructed HR data to the observed data. Furthermore, a slope-based Markov random field (MRF) regularization is used as the spatial regularization. The proposed method can simultaneously handle complex terrain features, noises, and data voids. Using the proposed method, we can reconstruct a seamless DEM data with the highest resolution among the input data, and an extensive spatial coverage. The experiments confirmed the effectiveness of the proposed method under different cases.

In this paper, we present two spatially adaptive nonlocal total variation methods for the speckle filtering of synthetic aperture radar (SAR) images. One is for the despeckling of SAR intensity images, and the other one is for full polarimetric SAR (PolSAR) images. Experiments were conducted on two simulated images and an airborne PolSAR images to illustrate the filtering performances, and the results show that the proposed methods effectively reduces speckle, retains edges and targets, and preserves the polarimetric scattering mechanisms.

Total variation has been used as a popular and effective image prior model in the regularization-based image processing fields. However, as the total variation model favors a piecewise constant solution, the processing result under high noise intensity in the flat regions of the image is often poor, and some “pseudo-edges” are produced. In this paper, we develop a regional spatially adaptive total variation (RSATV) model. Firstly, the spatial information is extracted based on each pixel, and then two filtering processes are respectively added to suppress the effect of “pseudo-edges”. After that, the spatial information weight is constructed and classified with k-means clustering, and the regularization strength in each region is controlled by the clustering center value. The experimental results, on both simulated and real datasets, show that the proposed approach can effectively reduce the “pseudo-edges” of the total variation regularization in the flat regions, and maintain the partial smoothness of the highresolution image. More importantly, compared with the traditional pixel-based spatial information adaptive approach, the proposed region-based spatial information adaptive total variation model can better avoid the effect of noise on the spatial information extraction, and maintains robustness with changes in the noise intensity in the super-resolution process.

The Earth Observing System of the National Aeronautics and Space Administration pays a great deal of attention to the long-term global observations of the land surface, biosphere, atmosphere, and oceans. Specifically, the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board the twin satellites Terra and Aqua plays a vital role in the mission. Unfortunately, around 70% of the detectors in Aqua MODIS band 6 have malfunctioned or failed. Consequently, many of the derivatives related to band 6, such as the normalized difference snow index, suffer from the adverse impact of dead or noisy pixels. In this letter, the missing or noisy information in Aqua MODIS band 6 is successfully completed using a robust multilinear regression (M-estimator) based on the spectral relations between working detectors in band 6 and all the other spectra. The experimental results indicate that the proposed robust M-estimator multiregression (RMEMR) algorithm can effectively complete the large areas of missing information while retaining the edges and textures, compared to the state-of-the-art methods.

With regard to quantitative remote sensing products in the visible and infrared ranges, thick clouds and accompanying shadows are an inevitable source of noise. Due to the absence of adequate supporting information from the data themselves, it is a formidable challenge to accurately restore the surficial information underlying large-scale clouds. In this paper, dictionary learning is expanded into the multitemporal recovery of quantitative data contaminated by thick clouds and shadows. This paper proposes two multitemporal dictionary learning algorithms, expanding on their KSVD and Bayesian counterparts. In order to make better use of the temporal correlations, the expanded KSVD algorithm seeks an optimized temporal path, and the expanded Bayesian method adaptively weights the temporal correlations. In the experiments, the proposed algorithms are applied to a reflectance product and a land surface temperature product, and the respective advantages of the two algorithms are investigated. The results show that, from both the qualitative visual effect and the quantitative objective evaluation, the proposed methods are effective.

In this paper, we propose a hyperspectral image denoising algorithm with a Spatial-spectral view fusion strategy. The idea is to denoise a noisy hyperspectral 3-D cube using the hyperspectral total variation algorithm, but applied to both the spatial and spectral views. A metric Q-weighted fusion algorithm is then adopted to merge the denoising results of the two views together, so that the denoising result is improved. A number of experiments illustrate that the proposed approach can produce a better denoising result than both the individual spatial and spectral view denoising results.

Hyperspectral images (HSIs) are often degraded by a mixture of various kinds of noise in the acquisition process, which can include Gaussian noise, impulse noise, dead lines, stripes, and so on. This paper introduces a new HSI restoration method based on low-rank matrix recovery (LRMR), which can simultaneously remove the Gaussian noise, impulse noise, dead lines, and stripes. By lexicographically ordering a patch of the HSI into a 2-D matrix, the low-rank property of the hyperspectral imagery is explored, which suggests that a clean HSI patch can be regarded as a low-rank matrix. We then formulate the HSI restoration problem into an LRMR framework. To further remove the mixed noise, the “Go Decomposition” algorithm is applied to solve the LRMR problem. Several experiments were conducted in both simulated and real data conditions to verify the performance of the proposed LRMR-based HSI restoration method.

Hyperspectral images (HSIs) provide abundant information to solve various kinds of problems like object identification and classification. However, HSIs often inevitably suffer many factors from various resources [1], such as imperfect imaging optics, sensor noise, and atmospheric effects, which degrade the acquired image quality [2]. Thus, HSI image super resolution reconstruction, used to achieve sub-pixel mapping, is an active research topic due to its effectiveness in improving the resolution of hyperspectral image. In the paper, a HSI super-resolution method, in which the different dictionaries are learnt for different bands and sparse structure from wavelength range with high correlation is regarded with similar sparse coefficients , is proposed to obtain the high-resolution image.

Total variation is used as a popular and effective image prior model in the regularization-based image processing fields. However, as the total variation model favors a piecewise constant solution, the processing result under high noise intensity in the flat regions of the image is often poor, and some pseudoedges are produced. In this paper, we develop a regional spatially adaptive total variation model. Initially, the spatial information is extracted based on each pixel, and then two filtering processes are added to suppress the effect of pseudoedges. In addition, the spatial information weight is constructed and classified with k-means clustering, and the regularization strength in each region is controlled by the clustering center value. The experimental results, on both simulated and real datasets, show that the proposed approach can effectively reduce the pseudoedges of the total variation regularization in the flat regions, and maintain the partial smoothness of the high-resolution image. More importantly, compared with the traditional pixel-based spatial information adaptive approach, the proposed region-based spatial information adaptive total variation model can better avoid the effect of noise on the spatial information extraction, and maintains robustness with changes in the noise intensity in the super-resolution process.

Total variation (TV) has been used as a popular and effective image prior model in regularization-based image processing fields, such as denoising, deblurring, super-resolution (SR), and others, because of its ability to preserve edges. However, as the TV model favors a piecewise constant solution, the processing results in the flat regions of the image being poor, and it cannot automatically balance the processing strength between different spatial property regions in the image. In this paper, we propose a spatially weighted TV image SR algorithm, in which the spatial information distributed in different image regions is added to constrain the SR process. A newly proposed and effective spatial information indicator called difference curvature is used to identify the spatial property of each pixel, and a weighted parameter determined by the difference curvature information is added to constrain the regularization strength of the TV regularization at each pixel. Meanwhile, a majorization-minimization algorithm is used to optimize the proposed spatially weighted TV SR model. Finally, a significant amount of simulated and real data experimental results show that the proposed spatially weighted TV SR algorithm not only efficiently reduces the “artifacts” produced with a TV model in fat regions of the image, but also preserves the edge information, and the reconstruction results are less sensitive to the regularization parameters than the TV model, because of the consideration of the spatial information constraint.

The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different spatial property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different spatial property regions should also be different. Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-spatial adaptive total variation (TV) model, in which the spectral noise differences and spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-spatial adaptive mechanism in the denoising process, and superior denoising results are produced.

Image super-resolution (SR) reconstruction has been a hot research topic in recent years. This technique allows the recovery of a high-resolution (HR) image from several low-resolution (LR) images that are noisy, blurred and down-sampled. Among the available reconstruction frameworks, the maximum a posteriori (MAP) model is widely used. In this model, the regularization parameter plays an important role. If the parameter is too small, the noise will not be effectively restrained; conversely, the reconstruction result will become blurry. Therefore, how to adaptively select the optimal regularization parameter has been widely discussed. In this paper, we propose an adaptive MAP reconstruction method based upon a U-curve. To determine the regularization parameter, a U-curve function is first constructed using the data fidelity term and prior term, and then the left maximum curvature point of the curve is regarded as the optimal parameter. The proposed algorithm is tested on both simulated and actual data. Experimental results show the effectiveness and robustness of this method, both in its visual effects and in quantitative terms.