Data-driven hyperspectral classification has gained significant attention across various applications, leading to the development of numerous novel neural networks for effective classification. However, existing spatial-spectral works often overlook the critical aspect of how training and testing samples are split, resulting in data leaks between these sets and suboptimal performance in real-world scenarios. To address this issue, we propose a non-overlapping sampling approach based on the extremal animals theory to achieve extreme data utilization, i.e., maximizing the number of testing samples. Specifically, it begins by determining the number of training samples n in each connectivity area according to the label map and pre-defined sampling ratio. Subsequently, it searches for all possible polyominos with the minimum perimeter p(n) and at least n pixels intersecting with the connectivity area. Finally, we remove unnecessary pixels according to the priority of their class in the connectivity area and degree in polyominos. Experimental results on Indian Pines validate the superior effectiveness of our proposed non-overlap sampling strategy.

Data-driven hyperspectral image classification has gained significant attention across various applications, leading to the development of numerous novel neural networks for effective classification. However, existing spatial-spectral works often overlook the critical aspect of how training and testing sets are split, resulting in data leakage between these sets and suboptimal performance in real-world scenarios. To address this issue, this paper proposes a non-overlapping sampling approach named PDUnS, drawing inspiration from the theory of extremal polyominoes to achieve peak data utilization, that is, to maximize the number of testing patches. Specifically, PDUnS begins by determining the number of training patchesnand a random seed point in each connected componentRof each class. Subsequently, among all constructed polyominoes that cover the seed point and intersect at leastnpixels withR, our PDUnS method searches for those with the minimum perimeterp(n). Finally, we remove pixels in the polyominoes that are not inR, and then delete pixels with a degree of 2 and farthest from the boundary ofRuntilnpixels are retained to construct training patches. Experimental results and in-depth analysis on the Indian Pines dataset reveal that PDUnS demonstrates an average 12.38% increase in the number of testing samples to the comparing method. This affirms the superior effectiveness of PDUnS in maximizing data utilization. The code will be available at https://github.com/Yanzi-S/PDUnS.

Spectral distortion severely limits detection performance in hyperspectral imagery, while feature learning with neural networks could provide sufficient capacity to enhance spectral consistency. This paper designs an end-to-end hyperspectral target detection (HTD) network based on transfer learning and nonlinear spectral synthesis (TLNSS). We first utilize bilinear mixture model (BMM) to synthesize nonlinear target and background spectra for training sample augmentation, which could better characterize ground objects in complex environments. Due to the mutual constraints between the quantity and diversity of the synthesized spectra, transfer learning is introduced to further address data insufficiency. Specifically, we propose an asymmetric autoencoder with a particularly designed multi-level loss to maximally distinguish the reconstruction residuals of background and target, where the multi-scale feature extraction sub-network is trained with abundant reference data, and the simple restoration sub-network is updated with the simulated spectra. To effectively reconstruct the input as expected, the features extracted from different blocks are complementarily integrated through residual attention. Lastly, we accumulate reconstruction residuals across all levels for final detection. The experimental results and ablation analysis of single-data detection on three hyperspectral images verify the superiority and effectiveness of the proposed method, and further cross-data detection consolidates the satisfactory tolerance of TLNSS to spectral variation.

Due to the side effects of asbestos on human health and environments, Italy has banned the use of asbestos-containing materials since 1992, but there are still illegal products with asbestos in daily life. In order to investigate the distributions of asbestos to facilitate its removal, this paper carries out asbestos identification with hyperspectral (HS) and panchromatic (PAN) data captured by the PRISMA satellite over Pavia, Italy. In this work, a pansharpening method with guided filter was used to inject more spatial details from 5m PAN to 30m HS. Then, the possible location of asbestos could be obtained by a bilinear sparse target detector (BSTD). Detection maps using BSTD are compared with that obtained by hierarchical constrained energy minimization (hCEM), ensuring the accuracy and reliability, also compared with the results using matched subspace detector with interaction effects (MSDinter) and adaptive MSD (AMSD) to verify the superiority of the bilinear sparse model.

Due to the side effects of asbestos on human health and environments, many countries have banned the use of asbestos-containing materials, but there are still illegal products with asbestos in daily life. In order to investigate the distributions of asbestos to facilitate its removal, this paper studies the feasibility of asbestos identification with HyperSpectral (HS) and panchromatic (PAN) data, taking images captured by the PRISMA and ZY1E 2D satellites over Pavia, Italy as examples. In this work, a pansharpening method with guided filter was used to improve HS image quality in terms of spectral fidelity and spatial details. Then, the possible location of asbestos could be obtained by a nonlinear target detector named BSTD. Considering high computational cost for large-scale remote sensing data processing, we further develop BSTD to its parallelized version (denoted as PBSTD). Given the groundtruth of asbestos over Pavia by the Regional Environmental Protection Agency-ARPA Lombardia, our PBSTD and several popular methods are evaluated from both qualitative and quantitative perspectives, showing that most algorithms could correctly detect large-size asbestos roofs, and the nonlinear PBSTD and MSDinter perform better in small-size asbestos identification than other linear detectors. However, the detection accuracy on small-size asbestos is insufficient in practical applications, which indicates that there are still issues to achieve accurate small-size asbestos identification using coarse-spatial-resolution spaceborne remote sensing.

The fusion of hyperspectral (HS) and panchromatic (PAN) images aims to generate a fused HS image that combines spectral information of the HS image with spatial information of the PAN image. In this article, we propose a multiresolution spatial–spectral feature learning (MSSL) framework for fusing HS and PAN images. The proposed MSSL transforms the existing deep and complex network into several simple and shallow subnetworks to simplify the feature learning process. MSSL upsamples the HS image while downsamples the PAN image and designs multiresolution 3-D convolutional autoencoder (CAEs) networks with a spectral constraint to learn complete spatial–spectral features of the HS image. MSSL designs multiresolution 2-D CAEs with spatial constraint to extract spatial features of the PAN image, with a low computational cost. In order to effectively generate the pansharpened HS image with high spatial and spectral fidelity, a multiresolution residual network is presented to reconstruct the HS image from the extracted spatial–spectral features. Extensive experiments are conducted on three widely used remote sensing data sets in comparison with state-of-the-art HS image fusion methods, demonstrating the superiority of the proposed MSSL method. Code is available athttps://github.com/Jiahuiqu/MSSL.

The binary hypothesis testing (BHT) is one of the most important models in hyperspectral target detection (HTD). However, this model is generally based on a linear mixture model (LMM) and might be inaccurate to reflect target and background characterizations in some scenes. This article presents a bilinear sparse target detector (BSTD) by applying the bilinear sparse mixture model (BSMM) to a popular BHT-based detection algorithm termed adaptive matched subspace detector (AMSD), which takes bilinear target–background interaction and sparse abundance into account. Moreover, as AMSD relies heavily on background subspace, we design a robust background subspace construction method. Specifically, we first classify each pixel into noise, border, or other particular instances according to its density, which is measured by jointly spatial–spectral distance. With the coarse classification map, a class-guided automatic background generation (CABG) process is introduced to reliably generate pure background samples. Detection statistics and component analysis on five real-world hyperspectral images verify the effectiveness of our BSTD method.

Convolutional neural networks (CNNs) have achieved prominent progress in recent years and demonstrated remarkable properties in spectral–spatial hyperspectral image (HSI) classification. However, conventional spatial-context-based CNNs commonly adopt the single patchwise scheme to represent the to-be-classified samples, which often fails to completely investigate the wealthy spectral–spatial information in complicated situations. For instance, it has great probability to cause misclassifications on the irregular or inhomogeneous areas, especially for the borders across different classes. To counteract this deficiency, we propose a unified multi-direction network (MDN) for HSI Classification (HSIC), which can exhaustively explore the abundant spectral and detailed spatial-context information through multi-direction samples. Additionally, considering the image-spectrum merged structure of the HSI, 3-D Squeeze-and-Excitation residual (3DSERes) blocks are devised in each stream of the framework to consecutively learn the spectral and spatial from low-level to high-level features. Specifically, 3DSERes can not only facilitate fluent gradient in backpropagation through skip connections, but also emphasize the significant spectral–spatial features and constrain the futile ones. This characteristic is beneficial to enhance the model’s generalization capability even with limited training samples. Furthermore, for properly aggregating the multi-direction deep features, we exploit the simple, yet effective attentional spectral prior (ASP) creatively through leveraging the original spectral correlations. Extensive experimental results on three benchmark data sets indicate that the proposed MDN-ASP can achieve promising classification performance compared to the state-of-the-art methods.

Despite recent advances achieved by deep learning techniques in the fusion of low-spatial-resolution hyperspectral image (LR-HSI) and high-spatial-resolution multispectral image (HR-MSI), it remains a challenge to reconstruct the high-spatial-resolution HSI (HR-HSI) with more accurate spatial details and less spectral distortions, since the low-level structure information such as sharp edges tends to be weakened or lost as the network depth grows. To tackle this issue, we creatively propose an edge-conditioned feature transform network (EC-FTN) in this article, which is mainly composed of three parts, namely, feature extraction network (FEN), feature fusion and transformation network (FFTN), and image reconstruction network (IRN). First, two computationally efficient FENs with 3-D convolutions and reshaping layers are employed to extract the joint spectral-spatial features of input images. Then, the FFTN conditioned on the edge map prior can fuse and transform the features adaptively, in which a fusion node and several cascaded feature modulation modules (FMMs) equipped with feature-wise modulation layers are constructed. Specifically, the edge map is generated via transfer learning, i.e., by applying the Sobel operator to feature maps of the red-green-blue (RGB) version of HR-MSI resulting from the pretrained VGG16 model without extra training. Finally, the desired HR-HSI is recovered from the transformed features through IRN. Furthermore, we elaborately design a weighted combinatorial loss function consisting of mean absolute error, image gradient difference, and spectral angle terms to guide the training. Experiments on both ground-based and remotely sensed datasets demonstrate that our EC-FTN outperforms state-of-the-art methods in visual and quantitive evaluations, as well as in fine details reconstruction.

Hyperspectral imagery with very high spectral resolution provides a new insight for subtle nuances identification of similar substances. However, hyperspectral target detection faces significant challenges of intraclass dissimilarity and interclass similarity due to the unavoidable interference caused by atmosphere, illumination, and sensor noise. In order to effectively alleviate these spectral inconsistencies, this paper proposes a novel target detection method without strict assumptions on data distribution based on an unconstrained linear mixture model and deep learning. Our proposed detector firstly reduces interference via a specifically designed deep-learning-based hierarchical denoising autoencoder, and then carries out accurate detection with a two-step subspace projection, aiming at background suppression and target enhancement. Additionally, to generate representative background and reliable target samples required in the detection procedure, an efficient spatial-spectral unified endmember extraction method has been developed. Performance comparison with several state-of-the-art detection methods and further analysis on four real-world hyperspectral images demonstrate the effectiveness and efficiency of our proposed target detector.

Target detection technique in hyperspectral imagery has been widely applied in various applications. However, its performance is severely limited by the useless interference contained in hyperspectral images (HSIs), mainly caused by the atmosphere, illumination, issues within the sensor itself, and some other factors. In this paper, we propose a hyperspectral target detector based on linear mixture model (LMM), which consists of three components. First, a hierarchical denoising autoencoder (HDAE) is specifically designed for redundant interference removal; then we apply an adaptive cluster approach to extract several representative background samples from the clean HSI; lastly, a target detector with subspace projection is developed for background suppression and target enhancement based on the clean HSI, representative background and prior-known target signatures. Experimental results on two real-world HSIs show the superiority of our proposed method, namely, the HDASP detector, comparing with other state-of-the-art target detection methods.

Deep learning-based hyperspectral target detection (HTD) is potentially hindered by the limited training samples and sensor-dependent transferability. To address this issue, we propose a novel semisupervised domain adaptive few-shot learning (SDAFL) model to adaptively transfer similarity/dissimilarity measurement from source domain with sufficient labeled samples to target domain in an adversarial manner, where source data and target data can be collected by different sensors, i.e., sensor-independent. In order to alleviate negative transfer, residual channel attention (RCA) and weighted domain adaptation (WDA) are used to automatically select representative features and assign easy-transferred samples with higher priority. In addition, we adopt modulated deformable convolution (MDConv) to make the receptive field fit image spatial structure and also introduce a discriminatively boosted loss (DBL) function based on the prior known target signature to further enhance feature distinction, where intraclass similarity is improved, while interclass similarity is suppressed. After extracting discriminative features through the SDAFL model, guided filter and t-distribution kernel are jointly used for spatial–spectral target detection (S2TD). It should be noted that only the spectral signature of the desired object is needed in the target domain. Experimental results and analysis on three real hyperspectral images (HSIs) verify the efficiency and superiority of our proposed sensor-independent hyperspectral target detection (SIHTD) method compared with other algorithms.

Target detection plays a core issue in hyperspectral remote sensing, but faces serious challenges of how to deal with the spatial and spectral redundancies and spectral variations. In this article, a novel network block is developed, called RFT-MSA block (abbreviated as RM), which includes the region-of-interest (RoI) feature transformation (RFT) and the multiscale-spectral-attention (MSA) module as to reduce the spatial and spectral redundancies simultaneously and provide strong discrimination. Furthermore, a deep spatial-spectral network (DSSN) is presented by stacking several RM and deconvolutional (DC) blocks for hyperspectral target detection in an unsupervised manner, and a feature loss term is investigated to simultaneously restrict the target to be sparse and minimize the energy of the background. The proposed algorithm mainly consists of three steps. First, an RoI map is detected using a classical detector (no statistic assumption is needed) with an edge-preserving filter. Then, the hyperspectral image (HSI) and the corresponding RoI map are considered as inputs to the DSSN for extracting the spatial and spectral feature of interest (SSFI). Finally, we apply the nearest neighbors (NNs) to the SSFI for detection-map refinement. The experimental results on one synthetic and three real HSIs demonstrate that the proposed algorithm outperforms other benchmark approaches in detection performance and robustness. In addition, further analysis also demonstrates the effectiveness of the proposed RM block.

Hyperspectral target detection has been widely used in practice, but its performance is seriously affected by spatial redundancy and spectral variation. In this paper, we design a novel network block including the region of interest (RoI) feature transformation (RFT) and convolution layer, which is called RS block, that can automatically attach different importance to pixels and provide guidance to discriminative feature extraction. Furthermore, a deep neural network (termed as RFTD) is proposed by stacking several RS blocks for hyperspectral target detection. In this way, the RS block enforces RFTD to concentrate on RoI feature extraction, and further increase distinction between target and hard-detected background (false alarm) pixels. Additionally, a constraint loss is introduced to exploit the sparsity and low rank property of hyperspectral images (HSI). Finally, we apply nearest neighbors (NN) for target detection in the feature space. Experimental results on two HSIs demonstrate that the proposed RFTD algorithm outperforms other detection methods.

In this paper, we propose a deep residual spatial attention network (DRSAN) for hyperspectral (HS) pansharpening. Different from the existing methods, our newly proposed method not only considers the spatial information of both the panchromatic (PAN) and the HS image simultaneously, but also adaptively learns more informative features of spatial locations for details enhancement, which mainly includes four steps. Firstly, the spatial details of the enhanced PAN image are obtained through the structure tensor. Then we extract the spatial information of the upsampled HSI by using the guided filter. The integrated spatial information of both PAN and HS images is subsequently fed into the DRSAN to map the residual HSI between the upsampled HSI and the reference HSI, where several residual spatial attention blocks (RSABs) are cascaded to exploit more useful details information. Finally, the fused HSI is generated by the summation of the upsampled HSI and the reconstructed residual HSI. Extensive visual and quantitative assessments validate the superiority of our proposed DRSAN over the state-of-the-art HS pansharpening methods.

Target detection (TD) is one of the major tasks in hyperspectral image (HSI) processing, and its performance is greatly affected by the background. Feature extraction (FE) has been an effective way to mine discriminative information, especially FE based on deep learning, which can learn the intrinsic properties of data to further improve the detection performance. Unlike supervised networks, unsupervised stacked sparse autoencoders (SSAEs) can learn deep and nonlinear features without any labeled data. However, SSAEs usually require a supervised fine-tuned model to obtain better discrimination, which is not feasible for TD, since the prior information is generally insufficient. In this letter, we introduce a distance constraint that is added to the SSAE to form a new distance constrained SSAE (DCSSAE) network. Specifically, the distance constraint maximizes the distinction between the target pixels and other background pixels in the feature space. Then, using the discriminative features learned from the DCSSAE, a simple detector using radial basis function kernel is derived for background suppression. Experiments on two HSIs demonstrate that the deep spectral features learned from the DCSSAE are more distinguishable, and our proposed detector, namely, the DCSSAE detector, outperforms several popular detectors, especially in background suppression.