Despite the proven effectiveness of deep learning technology in pine tree disease segmentation, acquiring a large volume of labeled data remains challenging and inefficient. Few-shot segmentation (FSS) uses a small amount of labeled data to guide the segmentation of unknown categories, further evolving into one-shot segmentation (OSS), which utilizes a single labeled sample to perform segmentation under conditions of extreme data scarcity. However, these methods are mostly applicable to natural images with clear boundaries and have not yet been applied to segmenting pine tree disease in autonomous aerial vehicle (AAV) remote sensing images. For this reason, we have designed the OSS model C2Net for the first time, which includes two main modules: 1) a prior contour awareness module (PCAM) that first generates a query image prior mask with contour response and then uses an iterative feature refinement unit (FRU) to refine features and accurately delineate the segmentation boundaries of pine tree disease and 2) an interclass relationship module (ICRM), which studies the vegetation index features of the support and query images, constructing importance weights that reflect the differences between categories, solving the visual similarity issue. Our experiments on field-collected and publicly available datasets demonstrate that C2Net excels in challenging OSS tasks, showing its ability to generalize across different sensor domains and various disease categories. Especially, on the field acquisition dataset, using just a single labeled pine tree disease image achieves an intersection over union (IoU) of 55.24% and an$F_{1}$of 71.24%.

Hyperspectral unmixing (HU) can obtain subpixel-level ground object information, which is crucial for the fine advancement of imaging spectrum processing technology. Deep learning (DL) has been widely used in HU recently because of its ability to deeply mine complex relevant features in data. Existing DL unmixing methods usually operate only in the original spatial-spectral feature domain. However, due to noise, spectral variation, and other factors, it is difficult to fully mine effective features and easy to interfere with by only relying on the original domain. To get over these obstacles, we propose an innovative stationary wavelet convolutional network (SWC-Net) for HU. Stationary wavelet transform (SWT) is introduced in SWC-Net to extend the original feature domain to feature domains with different frequencies, which promotes the multiview extraction of information. What is more, a new generative self-supervised feature learning strategy based on wavelet perspective (GSFL-W) is proposed for SWC-Net. More robust features can be obtained by GSFL-W by introducing noisy perturbations into high-frequency inputs and forcing the network to generate the original inputs. The proposed SWC-Net surpasses the advanced approaches by sufficient experiments on one simulated and three real hyperspectral datasets. The code is publicly available athttps://github.com/UPCGIT/SWC-Net.

Due to the limitations of imaging environment and hardware conditions, mixed pixels are common in hyperspectral images, which seriously affects the accuracy of land use coverage mapping. Subpixel mapping (SPM) decomposes mixed pixels to obtain the spatial distribution information of local object components inside the pixel, thereby breaking through the limitations of traditional pixel-level classification and achieving more accurate land use interpretation and refined mapping. Recently, deep convolutional neural networks have demonstrated their potential and effectiveness in SPM. However, in the SPM process, the multiscale spatial context information are not fully utilized in the process of using semantic information for network modulation, and the spatial representation at a more abstract level cannot be fully obtained. Therefore, in response to the above problems, this article proposes a multiscale semantic modulation hybrid convolutional network for SPM. The network obtains multiscale semantic information in semantics by constructing a multiscale semantic modulation module (MSSM) to modulate the backbone network and fully mine the spatial context information. Simultaneously, a hybrid convolutional module integrating, 2-D convolutional neural networks, 3D convolutional neural networks, and attention mechanisms is designed. This module captures joint spatial–spectral features while reducing model complexity and learns more abstract spatial representations to enhance the network’s performance in SPM. Experimental results show that this method outperforms the most advanced SPM methods on three public datasets and a produced wetland dataset, and the details of land cover categories are more prominent. The code and data will be released on GitHub upon acceptance:https://github.com/UPCGIT/MSMCNet

Vision transformers are widely used in hyperspectral image classification, with their core feature extractor being self-attention. Self-attention has a wider receptive field than convolution. However, existing vision transformers for the classification of hyperspectral images (HSIs) with a large number of bands generally suffer from high computational complexity and a large number of parameter requirements. In this paper, we propose an Ultra-lightweight Feature-compressed Multi-head Self-attention Learning Network (UFMS-LN), which mainly consists of a novel Compressed Feature Multi-Head Self-Attention (CF-MHSA), a Spatial Feature Enhancement- Enhancing Transformation Reduction (SFE-ETR) and a Spatial-spectral Hybridization-Receptive Field Attention Convolutional operation (SH-RFAConv). By effectively compressing feature maps in spatial-spectral dimensions, CF-MHSA achieves the same feature extraction capabilities as state-of-the-art self-attention mechanisms, and its floating-point operations (FLOPs) and parameters are two orders of magnitude lower than state-of-the-art self-attention mechanisms. SH-RFAConv is designed to emphasize local features, which have the ability to extract both spatial-spectral features simultaneously and have a wider receptive field than traditional convolutional operations. Furthermore, SFE-ETR is a preprocessing module for UFMS-LN that combines global spatial feature enhancement methods with Enhancing Transformation Reduction (ETR). Extensive experiments conducted on four benchmark HSI datasets have shown that this method achieves superior results compared to existing state-of-the-art HSI classification networks.

Cross-scene hyperspectral image classification (HSIC) leverages existing knowledge to categorize unknown scenes, aligning with the practical applications of remote sensing monitoring. However, spectral shifts across domains pose substantial challenges for this classification, aggravated by the insufficient number of labeled samples. Most existing methods predominantly address domain alignment from a singular perspective, rendering them inadequate to sustain robust classification performance in the presence of significant domain shifts. Additionally, although self-training can mitigate the labeling deficiency by leveraging unlabeled data, existing methods often fail to ensure the effective and accurate utilization of such data. Consequently, this article proposes a hyperspectral image (HSI) cross-scene classification architecture based on cycle self-training with joint adversarial (CSJA), which mitigates the impact of spectral shifts on cross-scene classification. Specifically, the proposed approach incorporates domain adversarial modules to reconcile domain distributions at varying granularities, coupled with a class adversarial module for joint adversarial alignment. Moreover, the cycle self-training (CST) module is devised to explicitly enforce pseudo-label generalization, thereby fully harnessing the informative content of the target domain. To effectively exploit both spatial and spectral information in HSIs and extract discriminative features, a convolutionally enhanced Transformer feature extraction network is proposed to generate feature-rich representations for both domains. Experimental evaluations on two publicly available cross-scene hyperspectral datasets and two self-made UAV hyperspectral datasets validate the superiority of the proposed algorithm.

The field of remote sensing (RS) image change detection (CD) has made significant progress, largely due to the powerful feature representation abilities of deep learning. However, traditional methods have not fully exploited the valuable information in differences. These methods often treat deep models as tools to extract features from individual images, which limits their ability to effectively describe differences. Additionally, many approaches tend to focus on spatial differences, while neglecting variations in the channel dimension. In this study, we introduce a novel Summator–Subtractor network for CD (${S}^{2}$CD), which adeptly captures subtle differences within both the spatial and channel aspects of bi-temporal images. The initial spatial and channel differences are derived through summation and subtraction operations on the bi-temporal images. The summator computes initial channel variations, while the subtractor captures initial spatial disparities. Transformers are then used to pull out meaningful differences in both spatial and channel patterns, allowing for a more nuanced understanding than methods relying solely on features from individual images. Finally, a heterogeneous modulation block integrates channel and spatial difference features, thus amplifying overall differences. Through extensive experimentation on four widely acknowledged CD benchmark datasets, our proposed${S}^{2}$CD method outperforms existing techniques, showcasing its superior performance and promising potential. The codes of this work will be available for the sake of reproducibility at:https://github.com/qianday/SSCD-CD.

In recent years, deep learning technology has emerged as the primary research focus in the field of hyperspectral anomaly detection (HAD) and has demonstrated satisfactory detection performance. Existing deep learning-based methods mainly utilize reconstruction errors as criteria for anomaly detection. However, they lack effective suppression of anomaly information in the background reconstruction process, and encounter challenges in addressing scenarios involving the coexistence of multiscale anomalies, which limits the performance of HAD. In order to reconstruct clean and reliable background images, this article proposes a Triple-UNet with dual-window convolution called TDWCNet for HAD. Specifically, we introduce a dual-window convolution that shields pixels within the inner window and only utilizes pixels between the inner and outer windows to reconstruct the central pixel. Based on the dual-window convolution, we construct the DWCBlock module, which serves as the core component for background reconstruction. To address the coexistence of multiscale anomalies, the Triple-UNet structure is designed, which organically combines three DWCBlock modules to gradually eliminate abnormal pixels that are inadvertently reconstructed due to inappropriate convolution kernels. Furthermore, adaptive mean-squared error (mse) and structural similarity index (SSIM) losses are employed to suppress anomaly reconstruction. Extensive experiments conducted on four publicly available datasets demonstrate that TDWCNet achieves satisfactory detection performance. The codes of this work will be available for the sake of reproducibility at:https://github.com/szc2277/TDWCNet-HAD.

The shipping industry is pivotal in transporting approximately 90% of the world’s goods, and it is characterized by evolving trends in vessel sizes and energy-efficient designs. Continuous advancements in technology for ship management have focused on detecting and analyzing anomalous and illicit vessels. In this study, we introduce ShipGeoNet, a model designed to extract geometric features from ships captured in Sentinel-1 synthetic aperture radar (SAR) images. ShipGeoNet employs a combination of convolutional neural networks (CNNs) and nonlinear regression techniques to extract various geometric features of ships from SAR imagery. The model follows a two-step approach. First, it utilizes a modified Mask R-CNN architecture and the ViTDet model to accurately detect ships, generating high-quality object masks for precise localization. In the subsequent step, a regression model utilizes the detected ship masks to extract key geometric attributes, including length, width, and orientation. The proposed nonlinear regression techniques are specifically crafted to address the complex nonlinear deformations inherent in SAR images. Through extensive experiments on a large-scale SAR dataset, ShipGeoNet demonstrates its efficiency and accuracy in ship size extraction and matching, outperforming existing methods. Developing the ShipGeoNet model opens up possibilities for future applications in maritime surveillance, navigation, and environmental monitoring.

The sparse unmixing (SU) technique is widely used in hyperspectral image (HSI) unmixing because it does not need to estimate the number of pure endmembers but directly obtains the spectra from known spectral libraries to construct the endmember matrix, which avoids the influence of endmember extraction on unmixing. However, some existing SU algorithms still have problems, such as insufficient consideration of abundance details and sensitivity to noise. In order to solve the above issues, this article proposes a graph Laplacian weighted robust SU (RSU) algorithm based on superpixels, which can better reconstruct abundance details and reduce sensitivity to noise. The coarse abundance is calculated based on the superpixel results, and then the global spatial prior weight is calculated. Then, weighted RSU is applied to each superpixel to achieve a combination of local and global cooperation to reduce sensitivity to noise. On this basis, in order to better reconstruct the abundance details, the spatial position information and spectral information between pixels within superpixels are used to construct a weighted map to represent the similarity between pixels. Finally, the alternating direction multiplier method (ADMM) is used to perform structural optimization on the superpixel scale, retaining the structural information of abundance and reducing the amount of calculation. Experiments are conducted on three simulated datasets and three real datasets, and the results show that the proposed algorithm outperforms state-of-the-art SU algorithms.

Object identification is one of the fundamental challenges in computer vision. Dealing with tiny, fuzzy, and widely dispersed objects is challenging despite tremendous improvements. This article introduces the detector in our model as a tool for detecting tiny objects in SAR (Synthetic Aperture Radar) photos. To overcome the aforementioned issues, the suggested model makes use of shallow layer information, spatial attention, and contextual information. To avoid missing the tiny object features, a shallow semantic information extraction model that incorporates low-level semantic data into the backbone has been designed. The original Neck has been replaced by an MSCFP (Multi-scale Context Feature Pyramid) in order to enhance the exploitation of lower levels of information and provide context information. The recognition of attention locations of various sizes is made possible by the introduction of a spatial attention system. Tests using open-source SSDD (SAR Ship Detection Dataset) datasets show that our model has effective identifying capabilities.

Precise and fast extraction of sea ice leads is the foundation for polar research and ship navigation. The accuracy of traditional methods for sea ice lead extraction is limited, and the efficiency of deep learning methods is difficult to guarantee. Besides, the tedious preprocessing steps complicate the application of existing methods. In this article, we proposed a lightweight semantic segmentation model based on the U-Net framework for sea ice lead extraction, which introduced lightweight blocks and a feature branch. Lightweight blocks took the place of the convolutional layers in U-Net to reduce the network parameters and increase operational speed. With the input of the contrast feature of horizontal-vertical (HV) polarization, the feature branch was used to improve the extraction precision and robustness. Besides, the combination of the lightweight blocks, feature branch and U-Net framework was beneficial to resist preprocessing. In the experiments, the performance of the proposed method on non-preprocessed Sentinel-1 dataset was better than that of the classical semantic segmentation method on preprocessed Sentinel-1 dataset in floating-point operations (FLOPs), parameters, frames per second (FPS), and accuracy evaluation. The results indicate that the proposed network is effective and lightweight for sea ice lead extraction from non-preprocessed data.

At present, how to achieve high precision hyperspectral image classification (HSIC) under the condition of few samples is a hot research issue. Metric-based meta-learning methods have proved to be very successful in this field. However, in terms of quantifying the dependencies between embedded features of hyperspectral samples, previous methods either only model marginal distribution and ignore joint distribution, limiting expressive capability of feature representation, or bring large computational cost though considering joint distribution. In this paper we propose a novel few shot learning (FSL) method based on Brownian distance covariance (BDC) for HSIC, which learns hyperspectral images’ representations by measuring the discrepancy between joint characteristic functions of embedded features and product of the marginals. In addition, a lightweight feature extraction network based on tied block convolution is proposed to better model cross channel correlation and aggregate global spectral-spatial features across channels. Extensive evaluations on several datasets show the effectiveness of the proposed method.

Due to the low resolution of hyperspectral images, the problem of mixed pixels is common, and hyperspectral unmixing is a crucial technology to solve the problem of mixed pixels. Among them, nonnegative matrix factorization (NMF) is widely used because it can simultaneously perform endmember and abundance estimations. As a variant of NMF, the archetype analysis (AA) is to find the most representative sample in the dataset, which has strong interpretability compared with NMF. However, traditional AA-based unmixing methods consider only one spectral curve to represent one class, ignoring endmember variability. To solve this problem, a manifold regularized sparse AA unmixing method considering endmember variability is proposed. In this paper, various spectra were included for each class to fully account for variability. In addition, considering the sparsity of abundance, L2,1regularization is used to impose sparse constraints on abundance, which ensures the sparseness of abundance. Furthermore, a manifold regularization constraint is introduced to use the underlying manifold structure of the data in unmixing, the construction of which is done by superpixel segmentation. The close relationship between the original image and the abundance is preserved. Experimental results on both synthetic and real hyperspectral datasets illustrate that the proposed method is superior to several multi-endmember extraction algorithms, AA-based algorithms, and advanced sparse NMF-based algorithms.

Autoencoders are widely utilized in hyperspectral unmixing as an unsupervised end-to-end learning model. In particular, convolutional autoencoder networks are popular for processing multidimensional hyperspectral features. Nonetheless, the traditional convolutional Autoencoder network’s receptive field is constrained in the unmixing task, and establishing the connection between the local spatial neighborhood and the local spectrum fails to improve unmixing performance significantly. To address these limitations, a bilateral global attention network based on both spatial and spectral information is proposed. It enables the network to obtain respective feature dependencies in the two dimensions and achieve optimal fusion of both features. The network comprises two information extraction branches. The spatial information extraction branch uses the Swin Transformer block to acquire the global spatial attention of the overall image, while the spectral information extraction branch designates a simplified spectral channel attention mechanism to gain spectral attention weight maps. The network’s efficacy is demonstrated through a comparative study using a synthetic dataset and two real datasets. The code of this work is available at https://github.com/UPCGIT/SSABN.

Superpixel segmentation has emerged as a prominent approach for simultaneous extraction of spatial-spectral features in hyperspectral imagery, exhibiting considerable efficacy in this domain. Although effective in spatial spectrum feature extraction, the existing feature extraction algorithms typically perform superpixel segmentation on a single band, failing to utilize the rich spectral and spatial information available across more bands. Moreover, current superpixel feature extraction methods lack scientific guidance for determining optimal multiscale parameters, which can lead to suboptimal segmentation and increased complexity of hyperspectral analysis. To overcome these limitations, this paper presents a novel band-by-band adaptive multiscale superpixel feature extraction method (BAMS-FE). The method comprises of two key components: a band-by-band superpixel-based feature extraction method and an adaptive optimal superpixel multiscale determination method. Firstly, the band-by-band superpixel-based feature extraction method performs superpixel segmentation for each band of hyperspectral images, thereby extracting joint spatial and spectral features. Secondly, the adaptive optimal superpixel multiscale determination method uses an unsupervised approach to determine the optimal multiscale superpixel segmentation parameters. Finally, the BAMS algorithm is obtained by combining the above two algorithms. The proposed algorithm is evaluated on five different datasets, and the results demonstrate its excellent precision and stability. With the top 99% principal components post PCA transformation or with raw, unprocessed hyperspectral datasets, stable and satisfactory classification performance is achieved by BAMS. Additionally, we compared its performance with several other state-of-the-art algorithms and found that it outperformed them in terms of accuracy. Our code will be publicly available at https://github.com/UPCGIT/BAMS-FE.

Hyperspectral anomaly detection (HAD) is crucial for identifying and analyzing abnormal objects in various domains. While existing methods have shown promising results by designing detection methods tailored to specific anomaly characteristics, there is a need for a highly versatile approach that can effectively handle anomalies, particularly those with large spatial sizes. In this article, we propose an end-to-end corner-visible network (CVNet) for unsupervised HAD. Specifically, we introduce a corner-visible convolution that leverages the statistical dependencies of the background within the receptive field while eliminating spatial correlations with potential anomalies for background generation. To address the grid effect caused by the corner-visible convolution, a background smoothing module is employed by using the conventional convolution. Furthermore, adaptive mean-squared error (MSE) and structural similarity index (SSIM) losses are employed to suppress anomaly reconstruction, resulting in a reliable reconstructed background map. Anomalies are identified through the residual of the original hyperspectral image (HSI) and the reconstructed background. Extensive experiments conducted on three publicly datasets demonstrate the effectiveness of our proposed method in handling different types of anomalies. The state-of-the-art performance showcases the versatility and applicability of CVNet in HAD. The codes of this work will be available for the sake of reproducibility athttps://github.com/Cloudynewbee/CVNet-HAD.

Oil film thickness (OFT) is an important indicator for estimating the amount of oil spill, and accurately quantifying the OFT is of great significance for loss assessment. In this paper, hyperspectral images (HSIs) of different OFTs (0.01-3.04 mm) through a ground experiment were obtained, and the spectral characteristics were analyzed. To address the issue of poor spectral separability for different OFTs, the 1DConvolutional Neural Network_Gate Recurrent Unit (1DCNN_GRU) model was developed for the quantitative inversion of OFT. It was validated through experiments on airborne Cubert-S185 HSI. The experimental results indicated that: (1) The proposed 1DCNN_GRU model effectively addressed the issue of reduced quantitative inversion accuracy resulting from poor spectral separability. The inversion results of it outperformed those of the SVR, CNN, and GRU models. Moreover, the optimal time for hyperspectral sensor to monitor OFT was at noon. (2) The proposed model using airborne hyperspectral data exhibited excellent inversion performance for OFT greater than 0.07 mm, especially with the best performance in 0.60-0.90mm. (3) The accuracy of HSI based OFT inversion assisted by brightness temperature (BT) data was superior to that of OFT inversion using single-source data. In particular, the proposed model had advantages in the feature level and decision level inversion of OFT in the ranges of 0.01-0.30mm and 1.00-3.04mm, respectively. This research provides technical support for the detection of OFT.

Autoencoders (AEs) are commonly utilized for acquiring low-dimensional data representations and performing data reconstruction, which makes them suitable for hyperspectral unmixing. However, AE networks trained pixel by pixel and those employing localized convolutional filters disregard the global material distribution and distant interdependencies, resulting in the loss of necessary spatial feature information essential for the unmixing process. To overcome this limitation, we propose an innovative deep neural network model named U-shaped transformer network using shifted windows (UST-Net). UST-Net prioritizes spatial information in the scene that is more discriminative and significant by using multi-head self-attention blocks based on shifted windows. Unlike patch-based unmixing networks, UST-Net operates on the complete image, eliminating inconsistencies associated with patches. Moreover, the downsampling and upsampling stages are used to extract HSI feature maps at different scales. This process generates a context-rich and spatially accurate abundance map without losing local details. The experimental results of one synthetic dataset and three real datasets demonstrate that UST-Net significantly outperforms both traditional and several other advanced neural network methods. Our code is publicly available at https://github.com/UPCGIT/UST-Net.

Convolutional neural network has achieved great success in hyperspectral image classification for its excellent local context modeling capabilities. However, the convolution operation with fixed-size local receptive fields is difficult to establish long-distance dependence in hyperspectral im-age. To address this problem, we propose a spatial-spectral unified transformer network, which utilizes self-attention mechanisms to extract global spatial and spectral features. In addition, in order to introduce local spatial and spectral information, the convolution operation is integrated into the network. Specifically, spatial and spectral convolutional embedding layers are designed to generate embeddings of spatial patches and spectral bands. Besides, depthwise convolution is exploited in the locally-enhanced feedforward layer to bring locality into transformer. Experimental results on two datasets demonstrate that our proposed network has greatly improved compared with other state-of-the-art methods.

Hyperspectral unmixing (HU) is widely used to process mixed pixels as an essential technology. Among them, the nonnegative matrix factorization (NMF)-based approach is one typical of the blind unmixing techniques, which can achieve endmembers and abundances simultaneously. Considering the physical meaning of the extracted endmembers, the archetypal analysis (AA) method constructs a new matrix decomposition structure with stronger interpretability than NMF. However, AA ignores the significant sparse property of abundance in unmixing. Therefore, we propose the L1sparsity-constrained AA algorithm for HU. To solve the new optimization problem, we explore a new optimization method for optimizing abundance. The alternating direction method of multipliers (ADMM) is used to increase the strong convexity and convergence of the problem. Then the fast gradient method (FGM) instead of traditional gradient descent is used to speed up algorithm convergence. The experimental results in both the synthesized and real datasets show that the proposed method outperforms several sparse NMF-based and AA-based methods.

In order to research the multi-year spatial-temporal distribution of Harmful Algal Bloom (HAB) in the South Yellow Sea of China, NDVI method was used to extract HAB from MODIS images after Cloud removal by a comprehensive threshold method explored in this paper. Then, the growth law and spatial-temporal distribution of HAB are analyzed by the method of standard deviation ellipse and superposition analysis. It shows that the periphery of the radial sand ridge area off the coast of Jiangsu Province is the main birthplace of the HAB. Entering the breeding period, the northward drift speed as well as the diffusion speed of HAB accelerates. Until late June, it invades the coast of Shandong Province. In the early stage of HAB growth, preventing the spreading along the northeast-southwest direction will effectively restrain the HAB.

Cloud interference often occurs in Enteromorpha prolifera (EP) extraction from MODIS images, with the purpose of solving this problem, a pseudo-invariant feature-based relative radiometric correction algorithm was proposed in this paper for cloud detection, and named PIF-RAC. The pseudo- invariant feature pixels were carried out to find the linear relationship of reflectance between target image and reference image in this algorithm. Then, the cloud detection threshold of the target image was corrected by the above established linear relationship and manual cloud detection threshold of the reference image. The experimental results show the automatic cloud detection effect of the proposed algorithm is close to that of the artificial threshold algorithm, which enables to effectively eliminate different kind of cloud interference for EP information from MODIS images. The PIF-RAC is an unsupervised algorithm with a high level of automation, which can be applied on EP disasters remote sensing operational monitoring.

Endmember extraction (EE) is one important step in hyperspectral unmixing. However, some EE methods under pure-pixel assumption may work badly for highly mixed data due to the complexity of image data. In this work, we propose a linear spectral mixing model-guided artificial bee colony (LSMM-ABC) method for EE to solve the problem under a highly mixed situation. The main innovative point of this work is that each employed bee in LSMM-ABC searches food source position guided by the LSMM, rather than with a neighbor food source position. What is more, this proposed LSMM-ABC is not confined to the pure-pixel assumption. The LSMM could help employed bees to find a better solution in endmember generation based on the ABC algorithm. Experimental results on both synthetic and real Cuprite data sets show us that the proposed LSMM-ABC method can improve the overall EE accuracy compared with the EE methods for highly mixed data.

Anomaly target detection for hyperspectral imagery (HSI) is one of the most important techniques, and many anomaly detection algorithms have been developed in recent years. One of the key points in most anomaly detection algorithms is estimating and suppressing the background information. This letter proposes a background-purification-based (BPB) framework considering the role of background estimation and suppression in anomaly detection. The main idea is the acquisition of accurate background pixel set. To prove the validity of the proposed framework, the BPB Reed-Xiaoli detector (BPB-RXD), the BPB kernel Reed-Xiaoli detector (BPB-KRXD), and the BPB orthogonal subspace projection anomaly detector (BPB-OSPAD) are proposed. Both the BPB algorithms focus on accurate background information estimation to improve the performance of the detectors. The experiments implemented on two data sets demonstrate that both BPB algorithms perform better than other state-of-the-art algorithms, including RXD, KRXD, OSP, blocked adaptive computationally efficient outlier nominators (BACON), probabilistic anomaly detector (PAD), collaborative-representation-based detector (CRD), and CRD combined with principal component analysis (PCAroCRD).

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.

In traditional anomaly detection algorithms, the background information is approximately described by whole hyperspectral imagery. However, the disparity between true and estimated background information would influence the performance of detection algorithms using background information. Considering this problem, a kernel background purification based anomaly target detection method is proposed in this paper. The main idea of the proposed method is to estimate background information more accurately. It contains two main steps: Firstly, the pure background pixel set extraction using the kernel-based method. Secondly, background covariance matrix estimation by extracted pure background pixel set. Experiments implemented on San Diego and PHI data indicate that the proposed method performed better than global Reed-Xiaoli detector (RXD), kernel RXD, and collaborative representation detector (CRD).

Spectral unmixing is one of the most important techniques for analyzing hyperspectral images. Many of the hyperspectral unmixing algorithms developed in recent years have been developed under an assumption that pure pixels exist, and some algorithms, such as N-finder algorithm (N-FINDR) and vertex component analysis (VCA), can only search data points in this case. However, the pure-pixel assumption may be seriously violated for highly mixed data. Whether a pure pixel exists or not, the endmember extraction can be regarded as an optimization problem. In this paper, we incorporate the linear mixture model (LMM) and particle swarm optimization (PSO) to develop LMM constrained PSO (LMMC-PSO) for endmember extraction from highly mixed data. The main contribution of the proposed method is that we redefine the particle motion rules. Each particle in LMMC-PSO moves in the search space according to the LMM, rather than with a velocity. The LMM is one kind of relationship between endmembers and mixed pixels, which can help guide efforts to build a path from mixed pixels to endmembers in PSO. The proposed algorithm was tested and evaluated with both synthetic and real hyperspectral data sets. The experimental results indicated that the proposed method obtains better results with highly mixed data than the algorithms of VCA, minimum volume constrained nonnegative matrix factorization, minimum volume simplex analysis (MVSA), robust MVSA, the convex analysis-based minimum volume enclosing simplex, and simplex identification via variable splitting and augmented Lagrangian.

Hyperspectral remote sensing could acquire hundreds of bands to cover a complete spectral interval, which deliver more information and allow a whole range of new and more precise applications. But vast data volume can cause trouble in computer processing and data transmission. Too many bands may cause interference for image processing and endmember variability is inevitable in hyperspectral data, which will affect the accuracy of interpretation. Band selection for hyperspectral image data is an effective way to mitigate the curse of dimensionality. In this paper, one hyperspectral band selection method based on endmember dissimilarity is proposed. This method used Mahalanobis distance as class separability criterion, and the spectral signature for each class is proposed by endmember extraction method automatically. Experiments on both synthetic and real hyperspectral data sets indicate that the proposed method outperformed the Minimum Estimated Abundance Covariance (MEAC) and Uniform Spectral Spacing (USS) method.

Target detection from hyperspectral images is an important problem. Many target detection algorithms have been proposed and have been widely used in real applications during the past decades. However, the performance of these algorithms is highly susceptible to the quality of the target spectrum. This paper proposes a multi-priori learning algorithm to learning the inherent spectral similarity and difference between multiple priori target spectra, which can alleviate the target spectral variation by boosting the priori target spectra. Experiments on two hyperspectral images illustrated the effectiveness of the proposed algorithm.

In this paper, endmember extraction algorithm is described as a combinatorial optimization problem. A novel quantum-behaved particle swarm optimization (QPSO) approach which employs quantum-behaved particle swarm optimization to find endmembers with good performance is proposed. As far as our knowledge, it is the first time that quantum-behaved particle swarm optimization is introduced into hyperspectral endmember extraction. In order to follow the law of particle movement, a high dimensional particles definition is proposed. The proposed algorithm was tested and evaluated by both synthetic and real hyperspectral data sets. Experimental results indicate that the proposed method get a better result compared to the algorithms of vertex component analysis (VCA), N-FINDR and discrete particle swarm optimization (D-PSO).