Deep learning for anomaly detection is one of the current hot topics in remote sensing. However, most deep learning-based methods require retraining or parameter fine-tuning when detecting different hyperspectral images, which increases the complexity of model usage and wastes computing resources. Although a few one-step detection methods exist, they are generally constrained by fixed spatial and spectral dimensions, limiting their flexibility in handling different hyperspectral images. More importantly, this constraint often disrupts the subtle and critical relationships between spectral bands, affecting the comprehensiveness and accuracy of image analysis. To overcome these issues, we propose an adaptive one-step hybrid network for hyperspectral anomaly detection (denoted as AdaptHAD). First, an adaptive sparsity module is constructed to assist in dividing samples in the training phase by utilizing the sparsity property of anomalies in hyperspectral images. Second, a data augmentation method that combines spectral information erasure with random noise is proposed to address the small sample problem. Third, a convolutional attention hybrid network with a transformer and convolutional neural network dual branches is designed to extract global and local features simultaneously to strengthen the model to identify anomalies and backgrounds. Extensive experiments on real-world hyperspectral images demonstrate the effectiveness of the proposed method. The code is available at https://github.com/ismeXQ/AdaptHAD.

Most of the current hyperspectral image classification (HSIC) methods assume that the interactions among all ground objects in hyperspectral images (HSIs) are static pairwise relationships. However, in real scenarios, multiple ground objects have complex spatial, spectral, or statistical correlations. These correlations are not limited to simple adjacent or pairwise relationships but also include complex higher order interactions involving three or more ground object categories. A hyperedge in a hypergraph can simultaneously connect multiple vertices, effectively capturing the multi-dimensional and high-order relationships among vertices. To address the limitations of the current mainstream methods in modeling the high-order interaction relationships of ground objects, a novel multifeature collaborative attention dynamic hypergraph convolutional (MDHGC) network is proposed to model the entire HSI and capture the high-order relationships among ground objects, thereby achieving accurate classification. Specifically, we designed a static–dynamic collaborative multiview hypergraph convolutional network based on differential attention to learn superpixel-level features, which allows stable and flexible learning of high-order interactions in HSI. To learn the complementary features of pixel-level HSIC, we introduce a branch based on convolutional neural neworks that includes multiscale feature extraction and global–local feature fusion. Comprehensive experiments have been conducted across four distinct datasets to rigorously evaluate the effectiveness of MDHGC.

Deep learning (DL) has received a lot of attention in hyperspectral anomaly detection (HAD) in recent years. While some progress has been made in boosting the generalization of DL-based HAD methods, existing methods still face limitations in labeled sample generation or transfer flexibility. To address these issues, we propose a contrastive self-supervised learning-based background reconstruction method for HAD (CSSBR). By constructing a self-supervised pretraining model based on a pixel- and patch-level masking strategy and a dual attention network (DAN) encoder, the pretraining model can learn general background representation without labeled sample generation. The DAN encoder is constructed by the vision transformer (ViT) and channel attention module (CAM) to extract the global context information and the correlation between spectra. A transfer learning model is constructed based on the DAN encoder and a lightweight decoder, which is simply fine-tuned by background reconstruction guided (BRG) loss to flexibly transfer the pretrained model to various HAD tasks. Experiments on four challenging hyperspectral datasets confirm that the CSSBR method outperforms other state-of-the-art methods.

Currently, the research on cross-scene classification of hyperspectral image (HSI) based on domain generalization (DG) has received wider attention. The majority of the existing methods achieve cross-scene classification of HSI via data manipulation that generates more feature-rich samples. The insufficient mining of complex features of HSIs in these methods leads to limiting the effectiveness of the newly generated HSI samples. Therefore, in this paper, we propose a novel single-source frequency transform (SFT), which realizes domain generalization by transforming the frequency features of samples, mainly including frequency transform (FT) and balanced attentional consistency (BAC). Firstly, FT is designed to learn dynamic attention maps in the frequency space of samples filtering frequency components to improve the diversity of features in new samples. Moreover, BAC is designed based on the class activation map to improve the reliability of newly generated samples. Comprehensive experiments on three public HSI datasets demonstrate that the proposed method outperforms the state-of-the-art method, with accuracy at most 5.14% higher than the second place.

As a popular deep learning (DL) algorithm, graph neural network (GNN) has been widely used in hyperspectral image (HSI) classification. However, most of the GNN-based classification algorithms are concentrated in the field of semisupervision, which heavily relies on the quantity and quality of samples. To solve this problem, we propose an unsupervised multiview graph contrastive (UMGC) feature learning algorithm to explore the deep semantic features of HSIs without being constrained by samples. First, we construct multiview adjacency matrixes from spatial and spectral directions. Second, the adaptive data augmentation method is used to selectively enhance the topology and attribute structure of the graph. Thereafter, features are extracted by using a contrastive loss to maximize the similarity between the two views. Finally, we tested the model’s performance based on multiple evaluation methods. Experimental results on three publicly available hyperspectral datasets show that the proposed UMGC can have better classification performance compared with other state-of-the-art unsupervised feature extraction (FE) methods.

Deep learning has shown great success in hyperspectral target detection (HTD). Due to the limited targets of interest in hyperspectral image (HSI), many deep learning based methods focus on spectral feature extraction by expanding samples, exhibiting low model transferability as well as high time consumption. To address these issues, we propose a novel spatial-spectral contrastive self-supervised learning-based HTD method with dual path networks (DPN-CSSTD). This method constructs a contrastive task using a HSI augmentation method and a DPN encoder on a large amount of unlabeled data, enabling the pretrained model with spatial-spectral discrimination ability. The DPN encoder utilizes the convolution neural network and the skip connections to improve the spatial-spectral feature extraction. Additionally, the proposed method constructs a transfer learning model with the pretrained DPN encoder and a newly added detector to improve the pretrained model transferability and the time efficiency of target detection tasks. The transfer learning model is fine-tuned via a proposed fine-tuning strategy with few selected samples to adapt to different TD tasks. Extensive experiments conducted on four challenging hyperspectral datasets demonstrate that the proposed method outperforms other state-of-the-art approaches.

Deep learning (DL) has made significant progress in hyperspectral target detection (HTD) in recent years. However, the existing DL-based HTD methods generally generate numerous labeled samples for network training, which may be impure or too similar to each other. Moreover, most methods with enormous parameters are trained and tested on the same dataset, resulting in single scenario applicability and significant computational consumption issues. To solve these issues, we propose a generative self-supervised learning (GSSL) pretraining model with spectral-spatial masking (S2M). The lightweight vision transformer (ViT) is utilized as the backbone to learn the universal feature representation of images without labeled samples. Subsequently, the pretrained model is transferred to various HTD tasks. The transfer learning model is constructed via the lightweight ViT and a fully connected (FC) layer and fine-tuned via a weighted binary cross entropy (WBCE) loss function and a small number of selected samples. We evaluate its effectiveness on four challenging hyperspectral datasets in terms of the GSSL pretraining model, the S2M strategy, and the WBCE loss function. Our methods achieve improvements in comparison to different pretraining models, masking strategies and loss functions. And our detection results also outperform other state-of-the-art approaches.

Cross-scene classification of hyperspectral image (HSI) based on single-source domain generalization (SDG) focuses on developing a model that can effectively classify images from unseen target domains using only source domain images, without the need for retraining. Most existing SDG approaches for cross-scene classification rely on convolutional neural networks (CNNs). However, the convolutional kernel operation causes the model to emphasize local object features, which can lead to overfitting on the source domain and limits its ability to generalize. Recently, methods based on the state space model (SSM) have demonstrated excellent performance in image classification by capturing global features across different image patches. Building on this inspiration, we propose a novel approach called dynamic token augmentation mamba (DTAM), which aims to explore the potential of SSMs in the cross-scene classification of HSI. The method gradually focuses on the global features of the image by constructing hidden states for HSIs unfolded into long sequences. To further enhance the global features of HSIs, we design a dynamic token augmentation (DTA) module to transform the sample features by perturbing the contextual information while preserving the object information tokens. Additionally, we introduce a loss of classified compensation combined with labels of random samples to suppress the excessive narrowing of the feature range learned by the model. Comprehensive extensive experiments on three publicly available HSI datasets show that the proposed method outperforms the state-of-the-art (SOTA) method. Our code is available athttps://github.com/Varro-pepsi/DTAM.

Object detection is a challenging task that aims to locate and classify instances in an image simultaneously. High-performing deep detectors are typically trained on extensive labeled datasets and will face the problem of accuracy degradation in different scenarios. Through domain adaptation, the detector can attain generalization, making precise detections even in the presence of different data distributions without labels. In this article, we propose a confidence-driven region mixing (CR-Mixing) framework, which is a novel approach that integrates the region-selective mixing (RSM) method into a mean-teacher framework with an unbiased adversarial module (UAM), addressing the domain-shift and knowledge transfer problems in the unsupervised domain adaptation (UDA) object detection task. First, we construct RSM that utilizes pseudodetection labels to compute object information at the region level and integrates this information in a balanced manner to maximize accurate foreground details for the detection model. This enables the detection model to adjust its focus to unlabeled domains gradually. Second, to acquire a backbone network with object-focused features, we develop the UAM method. This method is a multiscale alignment network designed to constrain the backbone network in learning domain-invariant features. We conducted extensive experiments on two benchmarks, and the final results show that the CR-Mixing model demonstrates significant effectiveness in robust UDA for optical remote sensing object detection tasks.

Building extraction from remote sensing images is extremely important for urban planning, land-cover change analysis, disaster monitoring and so on. With the growing diversity in building features, shape, and texture, coupled with frequent occurrences of shadowing and occlusion, the use of high-resolution remote sensing image (HRI) alone has limitations in building extraction. Therefore, feature fusion using multisource data has gradually become one of the most popular. However, the unique characteristics and noise issues make it difficult to achieve effective fusion and utilization. So it is very challenging to realize the full fusion of multisource data to achieve complementary advantages. In this paper, we propose an end-to-end multimodal feature fusion building extraction network based on segformer, which utilizes the fusion of HRI and LiDAR data to realize the building extraction. Firstly, we utilize the segformer encoder to break through the limitations of the traditional convolutional neural network with restricted receptive field so as to achieve effective feature extraction of complex building. In addition, we propose a cross-modal feature fusion (CMFF) method utilizing the self-attention mechanism to ensure the fusion of multisource data. In the decoder part, we propose a multi-scale up-sampling decoder (MSUD) strategy to achieve full fusion of multi-level features. As demonstrated by experiments on three datasets, our model shows better performance than several multisource building extraction and semantic segmentation models. The IoU for buildings on the three datasets reach 91.80%, 93.03%, and 84.59%. Subsequent ablation experiments further validate the effectiveness of each strategy.

Hyperspectral change detection (HCD) with limited training samples has attracted increasing attention in recent years. The current research works require to train a dedicated model for each dataset, resulting in limited generalization. Moreover, for the same dataset, a model that has been trained for binary change detection (CD) task usually needs to be retrained for multiclass CD task, thereby leading to low efficiency. We propose a cross-task meta-learning network with graph-enhanced attention module (CMGA) for both binary CD tasks and multiclass CD tasks, aiming to fully extract the spatial-spectral features of hyperspectral images (HSIs) and efficiently transfer the prior knowledge to accomplish various HCD tasks with limited training samples. In order to capture general prior knowledge, the multitask meta-optimization is designed to train the pretraining model with the ability of addressing cross-task new classes. Moreover, the diverse tasks are unified into one framework, which aims at identifying the target class from other classes to improve the model efficiency and generalization. The graph-enhanced attention (GA) module is developed to leverage the graph knowledge extract from the graph transformer (GT) module to enhance the pixel-level feature representation. Experiments on four hyperspectral datasets illustrated the effectiveness of the proposed algorithm.

Deep learning can fully exploit the potential information of data, which facilitates effective target-background separation for hyperspectral target detection (HTD). The deep learning model usually requires a large number of labeled samples, yet the available prior target spectra in the hyperspectral image (HSI) are extremely limited. In addition, network training also requires reliable input samples to ensure that the trained model has stronger data discrimination, but current detection methods often suffer from the problem that the background samples are impure. To address the above problems, we propose a lightweight convolutional neural network based on joint correlation distance constraint and density peak clustering (LCNN-CD) for HTD. First, the correlation distance between the prior target and HSI is calculated, and the pixels with high correlation are retained for expanding the target sample, which effectively handles the problem of insufficient target samples. Second, the density peak clustering algorithm is used to extract the main pure background samples, which effectively solves the problem that the background samples are sufficiently impure. Finally, a lightweight convolutional neural network model is designed, which is fed by the training samples (both target and background samples) to obtain the final detection results with low computational efficiency. The proposed method is compared with the classical and recently popular hyperspectral target detection methods on three real HSI datasets. Numerous experiments show that the proposed LCNN-CD method has better target detection performance and effectiveness.

Deep learning networks have demonstrate promising performance in high-resolution remote sensing images change detection (CD). The transformer can enhance the features and capture the global semantic relations, which has been used to solve the CD problem for high resolution remote sensing images with good results. However, the depth of the transformer is limited and the extracted features are not representative, which make the performance of the CD model unsatisfied. To fixed this problem, we propose a siamese network based on multiple attention and multilayer transformers (SMART) for CD in this paper. It is a siamese network containing three different modules, which can process bi-temporal images in parallel and extract enhanced features at different levels. The first is feature extraction module. It expresses the features as a certain number of high-order semantic features through the spatial attention module (SPAM), followed by the calculation of the semantic relations between these high-order semantic features using the transformer encoder, which greatly improves the computational efficiency. The second is feature enhancement module. It computes global semantic relations with self-attention module (SFAM). The multi-layer encoder gets the enhanced features at different levels by computing the relationship between features at each layer. The multi-layer decoder refines the bi-temporal features of each layer and projects them back to the original space. The third is fusion module. It uses the ensemble channel attention module (ECAM) to elaborate the feature differences at different levels. The proposed SMART model has been compared with some state of art CD methods in three publicly available data sets. The results confirm that SMART outperforms state of art change detection methods on several evaluation metrics. Our code is available at https://github.com/TwJ-IGG/SMART.

Self-supervised learning has shown remarkable success in image representation learning. Among these methods, masked image modeling and contrastive learning are the most recent and dominant methods. However, these two approaches will behave differently after being transferred into various downstream tasks. In this paper, we propose a RGB-elevation contrastive and image mask prediction pre-training framework. The elevation is normalized digital surface model. Then we evaluate the learned representation by transferring the pre-trained model into change detection task. To this end, we leverage the recently proposed vision transformer’s capability of attending to objects and combine it with the pretext task which is consist of masked image modeling and instance discriminant for fine-tuning the spatial tokens. Besides, the change detection task also requires us to do information interaction between the two temporal remote sensing images. To counter this problem, we propose a plug-in temporal fusion module based on masked cross attention and then we evaluate its effectiveness in three open change detection datasets in terms of initializing the supervised training weights. Our method achieves improvements in comparison to supervised learning methods and two mainstream self-supervised learning methods MoCo and DINO on change detection task. The results of our experiment also achieve state-of-the-art in four change detection datasets. The code will be available at URL.

Most transfer learning methods have the problem of insufficient distance constraint that plays a very important role in improving image classification performance. Therefore, this letter proposes a new method called joint distance transfer metric learning (JDTML) for remote-sensing image classification. First, the JDTML method establishes the constraints of marginal distribution, intraclass distance, interclass distance, and intraclass divergence based on the maximum mean discrepancy. Second, the objective function is to combine these constraints. So, JDTML can not only reduce the differences between the two domains on the whole and in each class, but also gather the samples of the same class and expand the distance from each class to the rest classes. By solving the objective function, the transfer metric matrix is obtained. Finally, the source and target domains are transferred to a common subspace for dimension reduction. The data after dimension reduction is used for classification, and the accuracy of classification is improved by iteration. The experimental results show that JDTML is more accurate than other methods compared.

Sparse unmixing (SU) has been widely applied to remotely sensed hyperspectral images interpretation. Compared with traditional unmixing algorithms, SU does not need to extract pure signatures (endmembers) from the image. The endmember matrix is constructed by directly selecting spectra from a known library which is used to estimate the fractional abundances associated with endmembers. This avoids the problem of extracting virtual endmembers without physical meaning. However, SU does not generally include spatial information, which may limit its performance. In order to address this limitation and include local spatial information, low-rank and sparse features in local regions can be exploited. In this paper, we include spatial information in the traditional SU algorithm by extracting low rank and spatial information based on superpixels, and further propose an algorithm named superpixel-based collaborative sparse and low-rank regularization for sparse unmixing (SCLRSU) to improve the performance of the traditional spatial regularization-based SU methods. In our proposed method, we combine superpixel segmentation and structural sparsity. Experiments are carried out on two simulated datasets and two real hyperspectral image datasets, and our results are compared with those obtained by traditional SU methods. Our results indicate that our newly proposed method provides very competitive performance.

Metric learning, which aims to obtain a metric matrix M such that samples from the same class are close to one another and samples of different classes are far from one another, is widely used in the field of hyperspectral dimensionality reduction (DR) and classification. Traditional metric learning is based on the Mahalanobis distance, which measures the similarity between samples via point-to-point distance, ignoring the structural features of the hyperspectral images (HSIs). To solve the above problem, we proposed clustered multiple manifold metric learning (CM3L), which obtains a manifold distance (MD), aimed at improving discrimination by introducing structural features of the HSIs and achieving good results. However, this manifold distance still has certain shortcomings in specific application situations. MD only considers the labeled data in the construction of the manifold and ignores the unlabeled data, resulting in the destruction of the manifold. Therefore, this article proposes a new spatial–spectral manifold distance (SSMD) to improve the performance of metric learning in hyperspectral DR and classification by maintaining the integrity of the constructed manifolds. The SSMD selects suitable neighboring points in the labeled and unlabeled data through the spectral–spatial information in order to participate in the construction of the manifold. Then, the distance between the manifolds is calculated to replace the traditional Mahalanobis distance. The results of seven sets of comparison experiments on three real HSI datasets demonstrate the effectiveness of SSMD in improving the classification results of HSIs.

Deep learning has achieved impressive results on hyperspectral images (HSIs) classification. Among them, both convolutional neural networks (CNNs) and graph neural networks (GNNs) have great potential for hyperspectral image classification. Supervised CNNs can efficiently extract hierarchical spatial-spectral features of hyperspectral images, but these methods face the problem of high time complexity as the number of network layers increases. Semi-supervised GNNs can rapidly capture the structural information of HSIs, while they cannot be well extended to hyperspectral image applications because of the process of adjacency matrix consuming large amount of memory resources. In this paper, we propose a fast dynamic graph convolutional network (dynamic GCN) and CNN parallel network (FDGC) for HSI classification. We first obtain two classification features by flattening and pooling operations on the results of the convolution layers, which fully exploits the spatial-spectral information contained in the hyperspectral data cube. Then a dynamic graph convolution module is applied to extract the intrinsic structural information of each patch. Finally, we can obtain the HSI classification results based on these spatial, spectral and structural features. By using three branches, FDGC can parallel process multiple features of HSI in a supervised learning manner. In addition, regularization techniques such as DropBlock and label smoothing are applied to further improve the generalization capability of the model. Experimental results on three datasets show that our proposed algorithm is comparable with the state-of-the-art supervised learning models in terms of accuracy while also significantly outperforming in terms of training and inference time.

Feature distortions of data are a typical problem in remote sensing image classification, especially in the area of transfer learning. In addition, many transfer learning-based methods only focus on spectral information and fail to utilize spatial information of remote sensing images. To tackle these problems, we propose spectral-spatial weighted kernel manifold embedded distribution alignment (SSWK-MEDA) for remote sensing image classification. The proposed method applies a novel spatial information filter to effectively use similarity between nearby sample pixels and avoid the influence of nonsample pixels. Then, a complex kernel combining spatial kernel and spectral kernel with different weights is constructed to adaptively balance the relative importance of spectral and spatial information of the remote sensing image. Finally, we utilize the geometric structure of features in manifold space to solve the problem of feature distortions of remote sensing data in transfer learning scenarios. SSWK-MEDA provides a novel approach for the combination of transfer learning and remote sensing image characteristics. Extensive experiments have demonstrated that the proposed method is more effective than several state-of-the-art methods.

The sparse representation has been introduced for hyperspectral anomaly detection methods. However, the window parameter tuning and anomaly contamination problems are still the main issues with the background dictionary. In order to solve these problems, this paper proposed the joint sparse representation and multi-task learning method (JSM) for anomaly detection. This method utilizes a global background dictionary construction method to avoid the above window parameter tuning and anomaly contamination problems. Besides, the multi-task learning technology is employed to explore the hyperspectral images similarity within adjacent single-band images. Experiments were carried out on two hyperspectral images, and it was founded that JSM method shows a better detection performance than the other anomaly detection methods.

With the high spectral resolution, hyperspectral image (HSI) can provide a wealth of information for image classification. Many classification methods utilize the training samples to classify the ground materials. However, the small sample problem is still urgent to be solved when considering the cost of labeling training samples. In order to solve this problem, this letter proposes a semisupervised classification method based on the simple linear iterative cluster (SLIC) segmentation for HSI. This method improves the SLIC method to better explore the spectral characteristic of HSI. It explores the learned superpixel map and initial classification map to select the pseudo-labeled samples (PLSs), which is expected to increase the effectiveness of PLSs. The final classification map can be obtained with the integrated labeled training samples and PLSs. Experiments were carried out on three HSIs, and it was founded that the proposed method generally shows a better classification performance than the other methods.

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).

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.

As sparse representation gradually obtains better and better results in the analysis of hyperspectral imagery and sparsity-based algorithms are becoming more and more popular, especially in target detection. However, these methods mostly assume an absolute equal contribution by all neighboring pixels while detecting the central pixel. There is no doubt that this approach is unsuitable for pixels located in heterogeneous areas. In this letter, to address this problem, spatially adaptive sparse representation for target detection in hyperspectral images (HSIs) is proposed. Neighboring spatial information is utilized by considering the different contributions of the distinct neighborhood pixels. The different weights are determined according to the similarity between the neighboring pixels and the central test pixel. The proposed algorithm was tested on two HSIs and demonstrated outstanding detection performance when compared with other commonly used detectors.

Target detection is playing an important role in hyperspectral image (HSI) processing. Many traditional detection methods utilize the discriminative information within all the single-band images to distinguish the target and the background. The critical challenge with these methods is simultaneously reducing spectral redundancy and preserving the discriminative information. The multitask learning (MTL) technique has the potential to solve the aforementioned challenge, since it can further explore the inherent spectral similarity between the adjacent single-band images. This letter proposes an independent encoding joint sparse representation and an MTL method. This approach has the following capabilities: 1) explores the inherent spectral similarity to construct multiple sub-HSIs in order to reduce spectral redundancy for each sub-HSI; 2) takes full advantage of the prior class label information to construct reasonable joint sparse representation and MTL models for the target and the background; 3) explores the great difference between the target dictionary and background dictionary with different regularization strategies in order to better encode the task relatedness for two joint sparse representation and MTL models; and 4) makes the detection decision by comparing the reconstruction residuals under different prior class labels. Experiments on two HSIs illustrated the effectiveness of the proposed method.

With the high spectral resolution, hyperspectral images (HSIs) provide great potential for target detection, which is playing an increasingly important role in HSI processing. Many target detection methods uniformly utilize all the spectral information or employ reduced spectral information to distinguish the targets and background. Simultaneously reducing spectral redundancy and preserving the discriminative information is a challenging problem in hyperspectral target detection. The multitask learning (MTL) technique may have the potential to solve the above problem, since it can explore the redundancy knowledge to construct multiple sub-HSIs and integrate them without any information loss. This paper proposes the joint sparse representation and MTL (JSR-MTL) method for hyperspectral target detection. This approach: 1) explores the HSIs similarity by a band cross-grouping strategy to construct multiple sub-HSIs; 2) takes full advantage of the MTL technique to integrate the sparse representation models for the multiple related sub-HSIs; and 3) applies the total reconstruction error difference accumulated over all the tasks to detect the targets. Extensive experiments were carried out on three HSIs, and it was founded that JSR-MTL generally shows a better detection performance than the other target detection methods.

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).

Anomaly detection is playing an increasingly important role in hyperspectral image (HSI) processing. The traditional anomaly detection methods mainly extract knowledge from the background and use the difference between the anomalies and the background to distinguish them. Anomaly contamination and the inverse covariance matrix problem are the main difficulties with these methods. The low-rank and sparse matrix decomposition (LRaSMD) technique may have the potential to solve the aforementioned hyperspectral anomaly detection problem since it can extract knowledge from both the background and the anomalies. This paper proposes an LRaSMD-based Mahalanobis distance method for hyperspectral anomaly detection (LSMAD). This approach has the following capabilities: 1) takes full advantage of the LRaSMD technique to set the background apart from the anomalies; 2) explores the low-rank prior knowledge of the background to compute the background statistics; and 3) applies the Mahalanobis distance differences to detect the probable anomalies. Extensive experiments were carried out on four HSIs, and it was found that LSMAD shows a better detection performance than the current state-of-the-art hyperspectral anomaly detection methods.

Hyperspectral images provide great potential for target detection, however, new challenges are also introduced for hyperspectral target detection, resulting that hyperspectral target detection should be treated as a new problem and modeled differently. Many classical detectors are proposed based on the linear mixing model and the sparsity model. However, the former type of model cannot deal well with spectral variability in limited endmembers, and the latter type of model usually treats the target detection as a simple classification problem and pays less attention to the low target probability. In this case, can we find an efficient way to utilize both the high-dimension features behind hyperspectral images and the limited target information to extract small targets? This paper proposes a novel sparsity-based detector named the hybrid sparsity and statistics detector (HSSD) for target detection in hyperspectral imagery, which can effectively deal with the above two problems. The proposed algorithm designs a hypothesis-specific dictionary based on the prior hypotheses for the test pixel, which can avoid the imbalanced number of training samples for a class-specific dictionary. Then, a purification process is employed for the background training samples in order to construct an effective competition between the two hypotheses. Next, a sparse representation-based binary hypothesis model merged with additive Gaussian noise is proposed to represent the image. Finally, a generalized likelihood ratio test is performed to obtain a more robust detection decision than the reconstruction residual-based detection methods. Extensive experimental results with three hyperspectral data sets confirm that the proposed HSSD algorithm clearly outperforms the state-of-the-art target detectors.

Sparse representation has achieved great success in the hyperspectral image processing field. However, with regard to target detection, the state-of-the-art sparsity-based algorithms are ad hoc and no different to a classifier. In this paper, a novel target detection algorithm is proposed, combining an elaborately designed sparsity model and the binary hypothesis statistics. With the strong similarity of the material spectra from the same class, sparse representation theory is explored by constructing hypothesis-designed dictionaries. Based on the local smooth property, locally optimized selection methods are employed for the background samples. For hyperspectral images, the pixels are usually assumed to obey a Gaussian normal distribution. Therefore, in this paper, a statistics-inspired sparsity model is established. The generalized likelihood ratio test is utilized to solve the model and build a statistics-inspired sparsity target detector (SISTOR). A number of experiments were conducted to illustrate the performance of the proposed algorithm.

In this paper, a new sparse representation-based binary hypothesis (SRBBH) model for hyperspectral target detection is proposed. The proposed approach relies on the binary hypothesis model of an unknown sample induced by sparse representation. The sample can be sparsely represented by the training samples from the background-only dictionary under the null hypothesis and the training samples from the target and background dictionary under the alternative hypothesis. The sparse vectors in the model can be recovered by a greedy algorithm, and the same sparsity levels are employed for both hypotheses. Thus, the recovery process leads to a competition between the background-only subspace and the target and background subspace, which are directly represented by the different hypotheses. The detection decision can be made by comparing the reconstruction residuals under the different hypotheses. Extensive experiments were carried out on hyperspectral images, which reveal that the SRBBH model shows an outstanding detection performance.

Target detection in hyperspectral imagery (HSI) is of great interest in the signal processing field. The key to the target detection methods lies in the proper estimation of a variety of measurement matrices that are estimated from the HSI. However, these matrices are usually ill conditioned due to the high dimension of the HSI or the inherent correlation between different image bands, which can result in inaccurate inverse matrices estimation. Therefore, how to handle the potentially inaccurate inverse calculation greatly affects the detection performance. This letter proposes a regularization framework that is suitable for the state-of-the-art measurement matrices used in target detectors. It adds a scaled identity matrix to these matrices in order to strengthen the stability of the inverse matrices, and, consequently, improve the detection performance. Extensive experiments were carried out on HSI that revealed the regularized detectors clearly outperformed the original detectors.