Hyperspectral anomaly detection is a crucial technique for recognizing abnormal pixels in hyperspectral images (HSIs), that is, those with distinct spectral characteristics from those of the surrounding background. Traditional methods always fall short in effectively leveraging the information regarding the spectral and spatial aspects of the dataset simultaneously, limiting their detection performances. This article proposes a novel framework using U-Net, termed hybrid convolution and transformer-based U-Net (HCT-Unet), which integrates convolution with a multihead attention mechanism in Transformer for enhanced hyperspectral anomaly detection. To ensure a more comprehensive understanding of spatial and spectral interactions, the HCT-Unet architecture capitalizes on the strengths of local feature extraction of convolutional layers and the capabilities of the long-range dependency modeling of Transformers. A key innovation of this framework is an error attention mechanism, which facilitates adaptive multiscale feature fusion and enhances the feature representation capacity. Furthermore, a new anomaly score calculation method is proposed, which combines reconstruction error with the pixelwise structural similarity index (SSIM) to determine pixel anomaly from both local structural preservation and global spectral consistency perspectives. Experiments carried out on seven different hyperspectral datasets reveal that the proposed method consistently outperforms the widely accepted state-of-the-art methods in hyperspectral anomaly detection.

Convolutional neural networks (CNNs) and transformers have achieved excellent classification performances in hyperspectral imagery (HSI) and light detection and ranging (LiDAR) land cover classification. However, for complex land covers, effectively characterizing the contextual information and spectral-spatial interaction features of HSI and LiDAR is crucial for improving classification accuracy. Motivated by this, this letter is dedicated to selective convolutional kernel mechanisms and spectral-spatial interactive transformer feature learning style, proposing a selective spectral-spatial aggregation transformer network, named S2ATNet. A convolution feature selected module (CFSM), which can dynamically capture the contextual features of various land covers, is first utilized in both of HSI and LiDAR branches. Afterward, a cascaded spatial-spectral learning and interactive fusion (CSLIF) block is designed for acquiring the nonlocal spatial-spectral characteristics in an interactive feature learning style. The learned features are fed into the max-average classification head (MACH) to obtain the final classification results. The effectiveness of the proposed S2ATNet is validated on two publicly available datasets. Codes are available athttps://github.com/RSIP-NJUPT/S2ATNet.git.

The fusion of hyperspectral and light detection and ranging (LiDAR) data could significantly improve land-cover classification performance. Most existing feature fusion methods focus on “late fusion” or “halfway feature interaction fusion” methods, which treat LiDAR and hyperspectral data as model inputs while overlooking the redundancy between hyperspectral imagery (HSI) and LiDAR features. In addition, the unique characteristics of HSI and LiDAR data, combined with the complexity of land-cover backgrounds, make it challenging to accurately describe their properties. High-order deep features, as a deep statistical representation, could effectively capture the statistical characteristics of these land covers. Based on this, this article focuses on the unique characteristics of HSI and LiDAR data, as well as the distinguishability of features, and designs a progressive hyperspectral and LiDAR collaborative classification method, named coarse-to-fine high-order network (CHNet). In the coarse stage, HSI data redundancy reduction and LiDAR feature reconstruction are performed in either the frequency domain or the spatial domain to ensure the effectiveness of subsequent feature fusion. The fine stage focuses primarily on selective feature fusion and discriminability enhancement, introducing gating mechanisms, deep expert systems, and high-order feature statistics. This approach enhances the discriminability of the fused features while simultaneously reducing their dimensionality. The proposed method, based on a “redundancy removal-feature learning” mechanism, captures more effective deep features by accounting for the different imaging mechanisms of multisource data and the complex background of land covers, ultimately improving the effectiveness of land-cover classification. Experimental results on three public datasets and one self-constructed dataset demonstrate that CHNet achieves superior performance. The code is available athttps://github.com/RSIP-NJUPT/CHNet.

The registration of synthetic aperture radar (SAR) and optical images is critical in multimodal remote sensing image fusion. In recent years, deep learning-based registration networks have been continuously introduced. However, owing to the significant disparities in viewing angles and radiometric properties between SAR and optical images, current deep learning methods struggle to fully exploit the physical properties of radar imaging. In addition, many existing matching networks typically perform only a forward pass, resulting in suboptimal model performance. This article proposes a cyclic iterative registration SAR mechanism network (termed as CIRSM-Net) for the registration of SAR and optical images. First, we design a learning module that integrates the radar equation with a microwave scattering model to capture deep features from SAR images, and design a corresponding scattering feature loss to aid in better generalization across various radar images. Then, to explore optimization methods for matching networks, this study proposes a strategy of multiple iterative optimizations within the matching network. Specifically, it integrates speeding-up radiation-variation insensitive feature transform (RIFT2) supervision in the backend matching network and iteratively optimizes the final output. Finally, during the iteration process, we propose an innovative matching loss function that combines the rotation invariance supervision of RIFT2 with iterative optimization techniques to enhance feature matching accuracy. Experimental results on both public and our own datasets additionally confirm the effectiveness and superiority of the proposed approach, demonstrating its significant potential for practical applications.

Multispectral image (MS) and panchromatic image (PAN) fusion, which is also named as multispectral pansharpening, aims to obtain MS with high spatial resolution and high spectral resolution. However, due to the usual neglect of noise and blur generated in the imaging and transmission phases of data during training, many deep learning (DL) pansharpening methods fail to perform on the dataset containing noise and blur. To tackle this problem, a variational optimization-guided two-stage network (VOGTNet) for multispectral pansharpening is proposed in this work, and the performance of variational optimization (VO)-based pansharpening methods relies on prior information and estimates of spatial-spectral degradation from the target image to other two original images. Concretely, we propose a dual-branch fusion network (DBFN) based on supervised learning and train it by using the datasets containing noise and blur to generate the prior fusion result as the prior information that can remove noise and blur in the initial stage. Subsequently, we exploit the estimated spectral response function (SRF) and point spread function (PSF) to simulate the process of spatial-spectral degradation, respectively, thereby making the prior fusion result and the adaptive recovery model (ARM) jointly perform unsupervised learning on the original dataset to restore more image details and results in the generation of the high-resolution MSs in the second stage. Experimental results indicate that the proposed VOGTNet improves pansharpening performance and shows strong robustness against noise and blur. Furthermore, the proposed VOGTNet can be extended to be a general pansharpening framework, which can improve the ability to resist noise and blur of other supervised learning-based pansharpening methods. The source code is available at https://github.com/HZC-1998/VOGTNet.

This paper proposes an image translation method based on multi-scale fusion GAN (MFS) network. In MFS network, there are two modules: optical image generation sub-network (OGS), optical image generation sub-network (OGS) and SAR image regressive sub-network (SRS). Firstly, we design a multi-scale fusion generator (MFG) to perform SAR-to-optical image translation and SAR image regression in OGS and SRS. MFG can extract multi-scale features at different scales, and fuse shallow and deep features, which enriches the semantic information of features. Combined with the PatchGAN discriminator, SAR-to-Optical image translation can be effectively realized. Finally, we conduct experiments on the SEN1-2 dataset, and the results show that our method performs better compared to the existing methods.

The vision transformer has been widely applied in remote sensing image scene classification due to its excellent ability to capture global features. However, remote sensing scene images involve challenges such as scene complexity and small inter-class differences. Directly utilizing the global tokens of transformer for feature learning may increase computational complexity. Therefore, constructing a distinguishable transformer network which adaptively selects tokens can effectively improve the classification performance of remote sensing scene images while considering computational complexity. Based on this, a second-order differentiable token transformer network (SDT2Net) is proposed for considering the efficacy of distinguishable statistical features and non-redundant learnable tokens of remote sensing scene images. A novel transformer block, including an efficient attention block (EAB) and differentiable token compression (DTC) mechanism, is inserted into SDT2Net for acquiring selectable token features of each scene image guided by sparse shift local features and token compression rate learning style. Furthermore, a fast token fusion (FTF) module is developed for acquiring more distinguishable token feature representations. This module utilizes the fast global covariance pooling algorithm to acquire high-order visual tokens and validates the effectiveness of classification tokens and high-order visual tokens for scene classification. Compared with other recent methods, SDT2Net achieves the most advanced performance with comparable FLOP-s (Floating Point Operations Per Second). The code will be available at https://github.com/RSIP-NJUPT/SDT2Net.

Local-global feature learning method based on deep learning has significantly improved the collaborative classification of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data. However, HSI encompasses numerous bands with significant interband correlations. Addressing how to efficiently capture spatial, spectral, and elevation information from hyperspectral and LiDAR data while considering data redundancy and enhancing feature representation of land cover will contribute to enhancing classification effectiveness. Combining frequency feature learning methods with convolutional neural networks (CNNs), transformers, and other architectures to construct an end-to-end feature learning network framework is an effective method. Therefore, this article proposes a frequency domain-based network (FDNet) for the classification of HSI and LiDAR data using a frequency local feature learning framework and a self-attention mechanism based on fast Fourier transform (FFT). FDNet could effectively capture local efficient frequency features of spatial, spectral, and elevation information in HSI and LiDAR data in an adaptive feature learning style, and embedding convolutional offsets into the frequency domain-based transformer network not only enhances local features but also effectively captures global semantic characteristics of land covers while reducing computational complexity. We validated the efficacy of FDNet across three publicly available datasets and a particularly challenging self-constructed dataset, denoted as the Yancheng dataset. The source codes will be available athttps://github.com/RSIP-NJUPT/FDNet.

Pansharpening refers to the fusion between a multispectral (MS) image with abundant spectral information and a panchromatic (PAN) image with high spatial resolution to obtain a high spatial resolution multispectral (HRMS) image. The traditional pansharpening methods often ignore the effect of path-radiation caused by scattering from different atmospheric components, and the few methods that introduce haze correction only calibrate each band of the MS image individually, without exploring the intrinsic correlation among different bands. To address this problem, low rank tensor completion pansharpening based on haze correction (LRTCP) is proposed. The haze-line prior is first introduced into the joint haze correction of MS and PAN images, and obtain the pre-modulated images with the help of the improved high-pass modulation (HPM) injection scheme. We then use tensor completion to simulate the degradation problem by applying low-tubal-rank tensor complementation to the process of reconstructing HRMS images, thus constructing a low rank tensor completion pansharpening model based on haze correction. Finally, the alternating direction multiplier (ADMM) is employed to find the solution of the proposed approach, producing the final fusion result. Comprehensive qualitative and quantitative assessment of reduced- and full-resolution datasets from different satellites shows that the proposed method outperforms the state-of-the-art methods.

As the most prevalent self-supervised representation learning (SSRL) model, the masked auto-encoder (MAE) has been gradually investigated in semi-supervised hyperspectral image classification (SHIC). However, the majority of the current approaches augment MAE merely from the application perspective or by introducing a weak regularization term, and do not comprehensively consider the challenges posed by the high intraclass variances and interclass similarities that often appear in hyperspectral image (HSI) data. In this article, we present a regularized MAE (RMAE) to address the aforementioned problems. Specifically, within the framework of MAE, we introduce a self-designed induced transformer block, using a small number of visible patches to learn the embeddings of patches with larger receptive fields. The learned embeddings are used to reconstruct the corresponding patches and an induced reconstruction loss is calculated. This strategy creates a much harder task for masked image modeling (MIM), and the induced transformer block is lightweight and imposes negligible computational burden overhead the underlying MAE framework. In addition, by rethinking the masking operations, we develop a masked convolutional neural network (MCNN), uncovering the principle of MAE and affirming the efficacy of RMAE. Finally, we present two metrics: the mean intraclass distance, and the mean interclass distance. Based on the metrics we give two criteria to evaluate the performance of an SSRL model, providing a new coordinate for the research in SSRL-based SHIC. Experiments conducted on four publicly accessible datasets show that RMAE outperforms state-of-the-art methods. The source code was powered by Jupyter and released athttps://github.com/swiftest/RMAE.

This paper proposes a multispectral pansharpening based on high-pass modulation injection model with difference factor (HPM-DF), which is dedicated to solving the problem of difference between multispectral image and panchromatic image acquired at different moments. In the high-pass modulation injection model, we introduce a difference factor and use the alternating direction method of multipliers (ADMM) to fully analyze the difference variability to derive the final fusion product. Experiments assessed at both reduced and full resolution show that the proposed method can acquire better performance than the traditional pansharpening methods.

In the most existing Hyperspectral Image (HSI) denoising methods, Poisson noise is first transformed into Gaussian noise through Anscombe transform and then remove it. However, transform errors may occur that affect the final denoising results when using Anscombe transform. In this paper, we propose a Poissonian hyperspectral image denoising method without using Anscombe transform (WUAT) to directly remove the noise of Poissonian HSI under the maximum a posteriori (MAP) model by finding the minimum value of the negative logarithmic Poisson log-likelihood combined with the total variation (TV). The experimental results show that the proposed method can acquire better performance than most state-of-the-art denoising methods.

The previous approaches based on statistical features or spatial features have achieved promising performance on pixel-wise high-resolution (HR) synthetic aperture radar (SAR) image classification, but these methods always cannot capture local spatial features and global statistical properties efficiently because of the complex spatial structural patterns and statistical nature in SAR patches. Inspired by this, we propose a deep joint statistical–spatial pooling network (DJSPNet), for HR SAR image classification, which combines a group second-order statistical feature learning (GSFL) block and an efficient feature-fusion style (EFS) into an end-to-end feature learning block. GSFL block is designed with a group second-order feature learning method in two steps, where the first step divides convolutional channels into several semantic groups. The second step collects second-order feature statistics by calculating pairwise feature interactions within each group. EFS models second-order attentional statistics between statistical characteristics and spatial features by polynomial kernel approximation and guides the discriminative feature activations in SAR patches. More specifically, both GSFL and EFS are stacked and plugged into the encoder stage of conventional U-Net for distinguishable feature learning. Experimental results suggest that the proposed DJSPNet gives better classification performance compared with related deep feature learning networks on a real TerraSAR-X dataset.

Poisson noise is one of the significant sources of noise present in hyperspectral imagery (HSI). In most of the existing denoising methods, Poisson noise is first transformed into Gaussian noise through the Anscombe transform and then removed. However, the use of Anscombe transform can give rise to transform errors that affect the final denoising results. In addition, blurs often contaminate the HSI during the imaging procedure, which makes it more difficult to remove the Poisson noise. In view of the above problems, under the maximum a posteriori (MAP) model, we propose a Poissonian blurred HSI denoising based on variable splitting and penalty technique (named as VSPT) to directly remove the Poissonian blurred HSI noise without using the Anscombe transform. By finding the minimum value of the negative logarithmic Poisson log-likelihood combined with the total variation (TV), the proposed method transforms the problem into two subproblems, which are easier to solve: 1) a TV regularized deconvolution problem and 2) an ordinary convex optimization problem. The experimental results show that the proposed VSPT method can effectively remove Poisson noise in HSI contaminated by blurs during the imaging procedure.

Most pansharpening methods refer to the fusion of the original low-resolution multispectral (MS) and high-resolution panchromatic images (PAN) acquired simultaneously over the same area. Due to its good robustness, multiresolution analysis (MRA) has become one of the important categories of pansharpening methods. However, when only MS and PAN images acquired at different times can be provided, the fusion results from current MRA methods are often not ideal due to the failure to effectively analyze multitemporal misalignments between MS and PAN images from different times. To solve this issue, MRA pansharpening based on variation factor for MS and PAN images from different times is proposed. The multi-resolution analysis pansharpening based on dual-scale regression model is first established, and the variation factor is then introduced to effectively analyze the multitemporal misalignments by using alternating direction method of multipliers (ADMM), yielding the final fusion results. Experiments with synthetic and real datasets show that the proposed method exhibits significant performance improvement compared to the traditional pansharpening methods, as well as the state-of-the-art MRA methods. Visual comparisons demonstrate that the variation factor introduces encouraging improvements in the compensation of multi-temporal misalignments in ground objects and advances pansharpening applications for MS and PAN images acquired at different times.

In this paper, a multispectral pansharpening based on high-pass modulation regression (HPMR) is proposed. Firstly, full-scale estimation is applied to improving the quality of the injection coefficient estimation. Then the injection coefficient is performed in the HPM which accomplishes the fusion by building a detailed relationship between the panchromatic (PAN) image and the multispectral (MS) image. Experiments on two data sets assessed both at reduced resolution and at full resolution show that the proposed method can acquire better performance than the state-of-the-art pansharpening methods.

Deep-learning-based methods have obtained satisfying results in polarimetric synthetic aperture radar (PolSAR) image classification. However, these methods require large numbers of labeled samples, which are usually time-consuming and high-priced for PolSAR images. To address this issue, a semisupervised method based on a 3-D convolutional neural network (3-D-CNN) using pseudo labels (PL-3-D-CNN) is proposed. First, the coherency matrix of PolSAR data is converted into a 6-D real-valued vector by a unitary transformation. Then, the K-means algorithm is utilized for generating pseudo labels. After that, labeled samples and pseudo labeled samples are fed into the PL-3-D-CNN model to extract supervised and unsupervised features. Finally, the supervised and unsupervised features are combined to improve classification accuracy. The proposed method is tested on both AIRSAR and RADARSAT-2 data sets. The results show that the proposed method is an effective method for PolSAR image classification and shows good performance under a small number of labeled samples. The source code for the PL-3-D-CNN model is available athttps://github.com/fangzheng-nuaa/PL-3D-CNN.

The feature learning methods based on convolutional neural networks (CNNs) have produced tremendous achievements in high-resolution (HR) synthetic aperture radar (SAR) image classification. However, the inherent speckle noise could weaken the effectiveness of the convolutional feature statistics. To effectively characterize the features of SAR land-covers under speckle noise, we propose a subspace wavelet encoding network (SWENet) trainable end-to-end and based on an encoder–decoder architecture for modeling the robust feature statistics in individual feature subspaces. We introduce a subspace encoder block at the end of the encoder stage and divide the entire feature space into a set of subspaces; the second-order statistics of all subspaces are concatenated. Then, the wavelet pooling block, suppressing the noise and keeping the structures of learned features well, decomposes the features into low-frequency (storing the basic object structures) and high-frequency components by Haar wavelet layer (HWL), and this block reconstructs the processed components using inverse IHWL during the upsampling stage. Especially, the wavelet pooling block is defined in each subspace for powerful feature learning. Experimental results on a TerraSAR-X image classification dataset suggest that our proposed SWENet yields a performance boost over its competitors.

Sub-pixel flood inundation mapping (SPFIM) could handle the mixed pixels to obtain the spatial distribution of flood inundation at sub-pixel scale. However, the spatial-spectral information of flood inundation used by SPFIM is usually constructed in a specified rectangular local window, and the number of spectral bands utilized by SPFIM is also little. To solve this problem, this paper proposes SPFIM based on spatial-spectral information in irregular regions (SSIIIR). In SSIIR, the extended random walker algorithm is used to calculate the spatial correlation in irregular regions to obtain spatial information, and the normalized model is constructed to calculate the all spectral bands in irregular regions to yield spectral information. With the help of spatial-spectral information in irregular regions, the flood inundation mapping result is improved. The experimental results show that SSIIIR produces the better flood inundation mapping result than the traditional SPFIM methods.

With the help of the auxiliary information provided by the appropriate prior fine spectral image (PFSI) in the same region, spatiotemporal super-resolution mapping (SSM) shows greater potential and better performance than the traditional super-resolution mapping (SM) models based on only monotemporal image. However, the temporal dependence of the existing SSM models usually describes the relationship between the coarse fractional images from original coarse spectral image (OCSI) and the fine fractional images from the PFSI, and the scale of temporal dependence information is not accurate and rich due to the different scales and properties of two fractional images. In addition, the existing SSM models usually do not consider point spread function (PSF) effect, resulting in affecting the accuracy of mapping result. To resolve the abovementioned issues, this letter proposes a general SSM model based on fine and coarse scales temporal dependence (FCSTD) by considering PSF effect. The experimental results demonstrate that the proposed model produces better mapping results than the traditional SM models, as well as the SSM models.

Subpixel mapping (SPM) based on multisource remote sensing fusion data (MRSFD) for land-cover classes, called SPM-MRSFD, is proposed in this letter. First, the original hyperspectral image and the auxiliary panchromatic image are fused to produce the high spatial and spectral resolution fused image by pan-sharpening technology. Second, the fused image with spatial–spectral information and the auxiliary digital surface model (DSM) of light detection and ranging (LiDAR) with elevation information are fused to obtain the MRSFD with spatial–spectral elevation information by feature fusion. Finally, the fractional images with the proportions of subpixels belonging to land-cover classes are derived by unmixing the MRSFD, and the classes labels are allocated to subpixels to obtain the final SPM result according to these proportions’ information. The main contribution of this work is that multiple types of auxiliary information (i.e., spatial–spectral elevation information) from MRSFD is fully utilized and the accuracy of SPM result is improved. Experimental results show that SPM-MRSFD obtains more accurate mapping results than state-of-the-art SPM methods.

Pansharpening technique is used to merge the original multispectral image (MS) with a high spatial resolution panchromatic image (PAN). Due to its robustness, the multiresolution analysis (MRA) is an important part of pansharpening. The scale regression model is effective for improving MRA. However, the existing MRA based on scale regression results into single-scale regression information, thus affecting the final pansharpening result. To address this problem, in this work, we propose a dual-scale regression-based MRA for pansharpening. First, we establish a scale regression-based model. Then, this model is improved using a high-pass modulation (HPM) injection scheme. Finally, the dual-scale information is added to the scale regression to construct the dual-scale regression for obtaining the final pansharpening result. We perform experiments using five datasets. The results show that the proposed method obtains a better pansharpening result as compared to various state-of-the-art MRA methods. In addition, the quantitative and qualitative analysis of the results shows that the proposed method achieves appropriate spatial and spectral resolution fusion. Therefore, it has a great potential in pansharpening technique.

Using the continuous-wave technology to replace the conventional pulse-mode, frequency-modulation continuous-wave (FMCW) synthetic aperture radar (SAR) has shown good potentials of reducing the weight of the system and the sensors' peak transmission power. In order to relax the requirements of data bandwidth and storage, and increase the swath, the SAR system will collect the downsampled data, which makes the traditional matched filtering (MF)-based method unable to recover the considered scene, leading to failed reconstruction. To solve this problem, this letter presents an FMCW SAR sparse imaging method based on the frequency-scaling algorithm (FSA). Experimental results on the real data show that compared with the MF-based FMCW SAR imaging algorithms, the proposed method can improve the recovered image performance effectively. For the sparse surveillance region, it can achieve accurate recovery even from the downsampled data. Because the computational complexity of the proposed method is in the same order as that of MF, the sparse imaging of large-scale scenes can also be realized in the FMCW SAR.

Due to the influences of imaging conditions, spectral imagery can be coarse and contain a large number of mixed pixels. These mixed pixels can lead to inaccuracies in the land-cover class (LC) mapping. Super-resolution mapping (SRM) can be used to analyze such mixed pixels and obtain the LC mapping information at the subpixel level. However, traditional SRM methods mostly rely on spatial correlation based on linear distance, which ignores the influences of nonlinear imaging conditions. In addition, spectral unmixing errors affect the accuracy of utilized spectral properties. In order to overcome the influence of linear and nonlinear imaging conditions and utilize more accurate spectral properties, the SRM based on spatial-spectral correlation (SSC) is proposed in this work. Spatial correlation is obtained using the mixed spatial attraction model (MSAM) based on the linear Euclidean distance. Besides, a spectral correlation that utilizes spectral properties based on the nonlinear Kullback-Leibler distance (KLD) is proposed. Spatial and spectral correlations are combined to reduce the influences of linear and nonlinear imaging conditions, which results in an improved mapping result. The utilized spectral properties are extracted directly by spectral imagery, thus avoiding the spectral unmixing errors. Experimental results on the three spectral images show that the proposed SSC yields better mapping results than state-of-the-art methods.

The second-order pooling manner, exploring higher feature statistics than the first-order pooling, has achieved impressive performance in scene classification. However, the object not only presents similarity but also exhibits diversified singularity on the synthetic aperture radar (SAR) image. These make the second-order pooling approaches to explore the single-view second-order feature statistics less adaptable for SAR scene classification. To solve this issue, an end-to-end training framework based on the multiview cross correlation attention network (MCAN) is proposed. The spatial and channelwise self-attention modules are first employed to model the interdependences between the spatial and channel dimensions of their convolutional features. Subsequently, the global spatial and channelwise covariance pooling layers are drawn into the MCAN, and then, they learn the spatial and channel cross correlations within the feature statistics, respectively. Finally, an iterative matrix square-root normalization layer, which owns the capability to fast computing approximate square root of the covariance matrix, is introduced for making the feature representation more discriminative. Experiments on the SAR data set from the TerraSAR-X images for scene classification demonstrate that the MCAN performs better than other related works.

In this letter, a novel subpixel mapping (SPM) based on extended random walker (ERW) (SPMERW) is proposed. First, the resolution of the original coarse remote sensing image is upsampled by bicubic interpolation. Second, the class proportions of subpixel are produced by unmixing the upsampled image. Irregular objects are generated by adaptive segmentation of the first principal component of the upsampled image. Third, the class proportions of the object are derived by averaged fusion of the class proportions of subpixel belonging to each object in the segmentation image. Object spatial dependence including the spatial information among and within the objects is obtained by the ERW algorithm. Finally, a class allocation method based on units of the object is utilized to obtain the SPM result according to the object spatial dependence. Experimental results on two remote sensing data sets show that the proposed SPMERW outperforms the state-of-the-art SPM methods.

Fixed coding style in bag of visual words (BOVW) model and strong spatial information in convolutional neural network (CNN) feature representation make the feature vector less adaptable for scene classification. With the purpose of extracting the learnable orderless feature for SAR scene classification, the high-order generalized orderless pooling network trained by backpropagation is proposed for learning the high-order vector of locally aggregated descriptors (VLADs) and locality constrained affine subspace coding (LASC), compared with the first-order feature coding style, the proposed network could learn high-order coding features by outer product automatically. Subsequently, for making the feature representation more powerful, the matrix normalization (square root) whose gradients are computed via singular value decomposition (SVD) and elementwise normalization are introduced into the proposed network. Finally, experiments on the SAR scene classification data set from TerraSAR-X image show the proposed networks achieve better performance than the state-of-the-art approaches.

Subpixel mapping based on the Hopfield neural network (HNN) is a technique to handle mixed pixels for obtaining the spatial distribution information of land cover. However, the original low-resolution remote sensing image may contain some uncertainties, such as the diversity of the land cover classes and the limitation of the resolution of the satellite sensor, the existing HNN is unable to fully utilize the prior information of the original image. In order to resolve this problem, an improved HNN (I-HNN) is proposed in this letter. In the proposed I-HNN, additional prior information of the original image is supplied by adding a new processing path to the existing HNN. To validate the effectiveness of the proposed method, two experiments are conducted on real hyperspectral images. The obtained results demonstrate that the proposed I-HNN outperforms the existing HNN. Moreover, the I-HNN does not require any auxiliary data.

Super-resolution mapping is an effective technique in mapping flood inundation for multispectral remote sensing image. However, the traditional super-resolution flood inundation mapping (SRFIM) is unable to fully utilize the spectral information from multispectral remote sensing image band. In order to resolve this problem, a novel SRFIM by supplying more spectral information (SRFIM-MSI) is proposed to improve mapping accuracy. In the proposed SRFIM-MSI, the spectral information from the multispectral band is calculated by the normalized difference water index (NDWI). A spectral term constituted by NDWI is added into the traditional SRFIM. The proposed method is evaluated by using two Landsat 8 OLI multispectral data from the study area in Cambodia. The obtained results demonstrate that the proposed SRFIM-MSI produces better results than the traditional SRFIM methods.

In this paper, a framework based on fine resolution thematic map, namely, interpolation then classification (ITC) is proposed. Firstly interpolation algorithm is applied in the original coarse hyperspectral imagery to derive a high-resolution imagery with generous prior information. Then fine resolution thematic map is derived from the high-resolution imagery by the available classification methods. Experiments on two real hyperspectral imagery showed that the proposed method produced higher accuracy result than interpolation-based soft-then-hard super-resolution mapping (I-STHSRM).

In this paper, a sub-pixel mapping (SPM) method based on super-resolution then spectral unmixing (SRTSUSPM) is proposed. In the proposed framework, firstly projection onto convex set (POCS) model with the endmembers of interest is applied to original imagery to obtain a high-resolution imagery; then the fraction images are derived from the high-resolution imagery by linear spectral mixture analysis (LSMA); finally hard attribute values on a per sub-pixel basis is implemented to achieve SPM. Experiments show that the higher mapping accuracy can be derived from the proposed SPM method.

In this letter, a novel subpixel sharpening for soft-then-hard subpixel mapping (SPM) is proposed. First, the fractional images for each class are, respectively, derived by spectral unmixing followed by spatial interpolation and by spectral interpolation followed by spectral unmixing. Bilinear and bicubic interpolation is used as the spatial and spectral interpolation methods. The fractional images for each class are then integrated together using the appropriate weighting parameter. Finally, the integrated finer fractional images are used to allocate hard class labels to subpixels. The proposed method is fast and does not need any prior spatial structure information. Experiments on two actual hyperspectral images show that the proposed method produces higher accuracy results than the existing algorithms. Moreover, both the spatial and spectral information is fully utilized to improve the accuracy of the SPM results.