Hyperspectral image (HSI) super-resolution reconstruction is a challenging ill-posed inverse problem, which seeks to enhance the spatial resolution of low-resolution hyperspectral images (LR-HSIs) by integrating complementary information from high-resolution multispectral images (HR-MSIs), ultimately generating high-resolution HSIs (HR-HSIs). Existing methods commonly employ residual connections and deep layer stacking to facilitate information propagation. While residual connections effectively preserve gradient flow, we observe that naively increasing network depth in high-dimensional spectral tasks can lead to feature redundancy and performance saturation. To address these challenges, this article presents a novel Butterfly residual network (BRNet) that incorporates spectral Transformers and depth-wise convolutions to optimize both accuracy and computational efficiency of hyperspectral super-resolution reconstruction from two perspectives: learning strategy and feature extraction. Regarding learning strategy, a recursive structure coupled with a fusion parameter generation technique is proposed to promote efficient feature fusion and enable adaptive network pruning, thereby reducing redundant information and enhancing computational efficiency. For feature extraction, spectral Transformer and depth-wise convolution are employed to capture spectral and spatial features, respectively, effectively leveraging their complementary advantages across different dimensions. A specialized spectral-spatial interaction (SSI) module is then incorporated to effectively fuse the extracted features, thereby enriching the diversity of network features. Additionally, the convolutional gated feed-forward network (FFN) is designed to bolster the network's ability to capture local features while significantly reducing the computational complexity. Experimental evaluations on three hyperspectral datasets demonstrate that the proposed method outperforms existing state-of-the-art super-resolution reconstruction methods across various performance metrics, validating its effectiveness and superiority.

Confronted with the increasing emergency of hyperspectral remote sensing categories in the dynamic environment, traditional classification models that depend on fixed-category labeled data encounter difficulties on new classes recognition. Novel class discovery (NCD) aims to discover unknown class-disjoint novel classes in an unlabeled dataset with the pre-existing knowledge of known classes. Notably, the critical goal of NCD is to ensure recognition accuracy of known classes while identifying new ones. In this paper, we propose a class-relation perceptive distillation with prototype-level clustering prediction network (CRPD-PCP) for NCD of hyperspectral image (HSI). The proposed framework comprises an initial training stage (ITS) and a novel class discovery stage (NCDS) with two essential modules. Specifically, we present the class relation perceptive distillation (CRPD) module, which imposes a similarity constraint on the prediction of the distribution of new class data over the models of two stages. With the CRPD operated on the NCDS, our model effectively captures class relation information in spectral-spatial domain between known and novel classes of HSI to avoid forgetting old knowledge. Besides, we establish the prototype-level clustering prediction (PCP) module to generate high-confidence pseudo-labels for unlabeled novel classes. To be specific, we progressively cluster samples with the same spectral angular distance from the perspective of prototypes, and the self-supervised prototype-level knowledge distillation strategy in PCP facilitates effective identification of new categories. Experiments conducted on four datasets demonstrate that the CRPD-PCP model generates superior performance compared to other NCD methods for HSI. Our code will be released at https://github.com/Chirsycy/CRPD-PCP.git.

Self-attention-based approaches that leverage global context information for hyperspectral image (HSI) classification have gained increasing prominence. Nevertheless, due to the assignment of equivalent attention weight to all the tokens (pixels or patches), the existing self-attention mechanism inadvertently prioritizes the non-label-specified information over the instinct label-specified information, which generates attention shifts and redundancy in HSI classification. To alleviate the mentioned barrier, we propose the center-specific perception transformer network (CP-Transformer), which is the first attempt to perform class-guided attention and filter interference factors for HSI classification feature representation. Specifically, the central-pixel focus attention module (CFA) is presented to compute the label-related attention between the center and other pixels. In this manner, CFA reduces computational complexity and closely aligns with the center-pixel labeling strategy. Besides, the spectral saliency focus attention module (SSFA) is developed to capture the spectral correlation by focusing salient bands to provide a beneficial supplement for spatial features. Moreover, the hierarchical integration network (HIN) constructs the inference network to integrate and rectify spatial-spectral features for HSI classification. The experiment results on four popular HSI datasets demonstrate that the proposed method achieves robust performance compared to other state-of-the-art methods. Our code will be released at https://github.com/Chirsycy/CP-Transformer.

Semantic segmentation in remote sensing images (RSI) assigns unique semantic labels to each pixel and plays a crucial role in real-world applications such as environmental change monitoring, precision agriculture, and economic assessment. Although convolutional neural networks (CNN) and Transformer-based models for semantic segmentation of RSI have achieved remarkable success, existing approaches still struggle to accurately detect weak edges and occluded objects due to the complexity and fuzziness of edges in RSI. To overcome this obstacle, we propose a novel probability-guided edge enhancement network (PEEN) for semantic segmentation of RSI, which is the first attempt to leverage the probability function to guide the segmentation model in performing edge prediction for RSI. Specifically, in the feature extraction stage of PEEN, we present a convolutional self-attention mechanism to enhance the global feature representation of the encoder-decoder network. In the edge enhancement stage of PEEN, we innovatively build an iterative probability-guided edge prediction module to refine edge prediction mathematically and iteratively. With the cooperation of the mentioned two stages, the proposed model yields precise segmentation of the objects and edge portions in RSI. Experiment results and analysis demonstrate that the PEEN model outperforms the existing popular CNN-based and Transformer-based models in semantic segmentation with 85.54% and 88.35% of Mean Intersection Over Union (mIOU) on the Vaihingen and Potsdam test datasets. Our code is available at https://github.com/Zyk517/PEEN.

Recently, the methods based on self-attention mechanisms have gained increasing prominence in hyperspectral image classification (HSIC). However, the existing self-attention mechanism suffers the challenge of attention shift and redundancy. To address the problem, we propose the center category focusing transformer network (CCSF-Transformer) for HSIC, which is designed to resolve attention shifts and redundancy by balancing the multiple category features. Specifically, the central-category-focused attention mechanism (CFA) is presented in the proposed framework to compute the category-matched attention between the center pixel and neighbor pixels, closely matching the center-pixel style labeling strategy, and reducing the computation complexity by excluding the computation between interference pixels. Besides, the spectral-salient-focused attention module (SFA) is developed to capture the spectral correlation, which concentrates on the salient bands and suppresses the expression of redundant bands. Moreover, the hierarchical integration network (HIN) is built to rectify spatial and spectral features The experiment results on two popular HSI datasets demonstrate that the proposed method achieves robust performance compared to other state-of-the-art methods.

Change detection (CD) in remote sensing imagery is identified as a pivotal task in the field of Earth observation, while it usually confronts the dilemma of intricate data and minor alterations. To address the stated challenge, this letter presents an innovative frequency-temporal attention network for CD (FTAN), which incorporates two advanced modules including the multidimensional convolutional frequency attention module (MCFA) and the interactive attention module (IAM). Specifically, the MCFA module is essential for enhancing sensitivity in CD by merging multiscale spatial and frequency domain features. As a supplement to MCFA, the IAM aggregates category-related tokens and processes cross-attention information from different time phases. The seamless integration of MCFA and IAM empowers the FTAN network with enhanced capabilities to detect minor regions and edges accurately. Experiments on datasets like LEVIR-CD and DSIFN-CD demonstrate superior performance by outperforming existing models in F1 scores and IoU metrics. Our code and pretrained models will be released athttps://github.com/chirsycy/FTAN.

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

Hyperspectral image classification (HSIC) models have made remarkable progress in the last decade. Nevertheless, the downsized mapping in the convolutional neural network (CNN) and down-sampled mechanism in the transformer-based approach amplify the loss of hidden knowledge in the subpixel that encompasses crucial yet unseen features within a single pixel. Considering this aspect, the mentioned popular solutions for HSIC contradict the inherent characteristic of hyperspectral data. To address this issue, we rethink the size factor in CNN and propose a novel spatial mapping expansion with spectral compression (SMESC) network for HSIC. Specifically, the SMESC builds a mapping expansion network to mine unseen information in subpixels with enlarged feature maps. A channel modulation residual block (CMRB) is developed to compress spectral redundancy and promote salient channels with modulation information. Moreover, we design a multiple-size training strategy to substitute the traditional multiple feature extraction (FE) branches and improve the model adaptation to the different sizes of the testing samples. The extensive experimental results and analysis of four hyperspectral image (HSI) datasets demonstrate the superiority of the proposed architecture compared to other advanced HSIC methods. Our code will be released athttps://github.com/Chirsycy/SMESC.

Thermal infrared hyperspectral imagery presents a superior capability for capturing intricate spectral details of atmospheres and ground objects compared to multispectral images, thus offering a more nuanced dataset for land surface temperature (LST) retrieval. However, extensive inter-band correlations pose computational challenges and undesirable “dimension disaster” problem. To address this issue, this paper proposes a purpose-built framework of thermal infrared hyperspectral band selection using graph neural network for LST retrieval. Specifically, the thermal infrared hyperspectral data is firstly mapped onto a graph topology, followed by feeding it into a graph attention module with brightness temperature constraints to extract band features. Following this, the extracted band features undergo a comprehensive analysis through a multi-scale convolution module consisting of convolution kernels with multiple sizes, which has more variety and larger receptive fields for calculating the correlation between different bands features, assigning different weights to each band. Finally, a weight selection module is designed to filter the bands based on their assigned weights, creating a subset of bands with greater significance for LST retrieval. Training the designed model, 65100 observations are simulated utilizing MODTRAN, 80% allocated for training and 20% for testing. The experimental results validate the effectiveness of the proposed model, with a Root Mean Square Error (RMSE) of 1.85 K in practical applications on IASI imagery. This accomplishment substantiates the model’s capacity to reliably employ a judiciously selected subset of thermal infrared hyperspectral bands for LST retrieval applications, thus offering a promising contribution to the advancement of thermal infrared hyperspectral image processing methodologies.

Hyperspectral images provide spectral curves that reflect the "fingerprint" properties of substances, making them suitable for many applications. Thanks to the rapid development of computing resources, deep learning algorithms can significantly improve the cognitive ability of the network by extracting hidden features, and have been successfully applied to hyperspectral image processing, such as classification and detection. In this paper, a new hyperspectral target detection model based on one-dimensional generative adversarial networks (1D-GAN) is proposed. The proposed 1D-GAN network is designed to extract HSI features, and the probability is calculated accordingly whether the pixel to be detected is a target or background. In order to capture the spatial features, the guided filter is then used to obtain the final detection map. Performance comparison with several state-of-the-art methods demonstrate the effectiveness and efficiency of the proposed 1D-GAN algorithm.

Hyperspectral image (HSI) has attracted much attention because of its rich spectral information. However, due to the limitation of imaging hardware conditions, it is often difficult to directly obtain a high spatial resolution hyperspectral image (HR-HSI). To improve the resolution, it is an economical and effective method to fuse the hyperspectral image with the high spatial resolution multispectral image (HR-MSI) collected from the same scene. In recent years, with the development of deep learning, the convolutional neural network (CNN) based models have been applied to solve the super-resolution reconstruction of hyperspectral images. However, limited by the convolution kernel size, the receptive field of CNN is relatively small with more attention to the local information of the image. In order to solve this problem, this paper proposes a Swin Transformer based super-resolution reconstruction (STSR) network for hyperspectral images. Specifically, Swin Transformer structure is innovatively used in STSR as the skeleton of the network, where the Swin Transformer residuals are used to extract the global spatial feature information in the image. In addition, in order to retain the spectral details in the process of super-resolution reconstruction, a spectral attention module is introduced to preserve the original spectral information. The experimental results show that the high-resolution hyperspectral images fused by the proposed STSR method are superior to the comparison method in terms of vision and quality, which proves the superiority of this method.

Multi-exposure image fusion (MEF) technique is the most widely used method to obtain high dynamic range (HDR) images. Inspired by the recent successful application of Transformer in image processing, a hybrid dense connection network based on CNN and Transformer is proposed for MEF in this paper. Considering the importance of texture details to the multi-exposure image fusion task, shallow features containing rich texture details is also added to each dense layer, which are extracted by the pre-trained RepVGG. In addition, the dynamic weight calculation module is improved, so that different source images can obtain finer weight in the calculation of the loss function. Experiments are conducted on the dataset provided by MEFB, and both qualitative and quantitative comparisons show that the proposed method can achieve better results compared with the state-of-the-art algorithms.

Deep learning-based hyperspectral target detection (HTD) methods are limited by the lack of prior information. Self-supervised learning is a kind of unsupervised learning, which mainly mines its own self-supervised information from unlabeled data. By training the model with such constructed valid posterior information, a valuable representation model can be learned and can get rid of the dependence of deep models on prior information. To this end, this article proposes a self-supervised spectral-level contrastive learning-based HTD (SCLHTD) method to train a model with spectral difference discrimination capability for HTD in a self-supervised manner. First, the hyperspectral images (HSIs) to be detected are sampled in odd and even bands, and the obtained band subsets are then used to train the corresponding adversarial convolutional autoencoders. Feature extraction part of the trained encoder is then used as the data augmentation function, where the positive and negative pairs are constructed through data augmentation, and the backbone is used to extract the representative vectors of the augmented samples. Second, the representative vectors are mapped to the spectral contrast space using spectral contrastive head, where the similarity and dissimilarity of spectra are learned by maximizing the similarity of positive pairs while minimizing the similarity of negative pairs, so that the backbone can discriminate spectral differences. Finally, aiming at suppressing the background, edge-preserving filters are used in conjunction with space information to process the detection results acquired by utilizing spectrum information via cosine similarity to generate the final detection results. Experimental results illustrate that the proposed SCLHTD method can achieve superior performances for HTD.

Hyperspectral unmixing could provide decomposition for small units in hyperspectral image, allowing accurate analysis of ground objects. Unfortunately, interference such as noise and spectral variability prevalent in hyperspectral data poses a serious challenge for it. Accordingly, this paper proposes a spectral-spatial anti-interference nonnegative matrix factorization (NMF) algorithm (SSAINMF), which improves the performance of spectral unmixing from both spectral and spatial perspectives. Specifically, the original data is analyzed and transformed into a statistical domain where the information of each dimension can be re-expressed, followed by a proof of restricted isometric and restricted isospectral properties for endmembers and abundances between the original domain and the transformation domain. To obtain more reliable endmembers, weighting is then applied to each dimension in the transformation domain depending on the priority coefficients quantified by their contribution to data representation, with the influence of anomalous and noisy data weakened and the priorities of low-rank information emphasized. Finally, superpixels are exploited to induce local similarity and structural sparsity of abundances within the neighborhood, which reduces the sensitivity to spatial noise and spectral variability. From experimental results on synthetic and real data sets, the proposed SSAINMF has demonstrated effectiveness in decomposing mixed pixels, with better robustness.

With the development and progress of deep learning, the use of deep learning technology for hyperspectral target detection has achieved excellent results. However, most deep-learning-based methods do not effectively suppress background. This paper presents a contrastive learning-based hyperspectral target detection (CLHTD) for this purpose. The positive and negative pairs are constructed through data augmentation, and the backbone is used to extract the representative vectors of the augmented samples. Then the representative vectors are mapped to the spectral and the cluster contrast space using their corresponding contrastive head, respectively. In the contrast space, the similarity and dissimilarity of spectra and clusters are learned by maximizing the similarity of positive pairs while minimizing the similarity of negative pairs, to increase the difference between the representative vectors of target and background. Finally, the detection result is obtained through the cosine distance. Experimental results illustrate that the proposed CLHTD algorithm can achieve superior performances for hyperspectral target detection.

A novel multi-scale fusion maximum entropy subspace clustering (MFMESC) for hyperspectral image (HSI) band selection is proposed in this paper. Subspace clustering is combined as a self-expression layer with stacked convolutional autoencoder, so that subspace clustering working in linear subspaces can deal with complicated HSI data with nonlinear characteristics. Multiple fully-connected linear layers are inserted between the encoder layers and their corresponding decoder layers to promote learning more favorable representations for subspace clustering. A multi-scale fusion module is designed to guide the fusion of multi-scale information extracted from different layers to learn a more discriminative self-expression coefficient matrix. Furthermore, the maximum entropy regularization is introduced in the subspace clustering to promote the connectivity within each subspace. Experimental results demonstrate the superiority of the proposed model against state of-the-art methods.

This letter proposes an unsupervised band selection (BS) algorithm named residual driven BS (RDBS) to address the lack ofa prioriinformation about anomalies, obtain a band subset with high representation capability of anomalies, and finally improve the anomaly detection (AD). First, an anomaly and background modeling framework (ABMF) is developed via density peak clustering (DPC) to pre-determine the prior knowledge of the anomalies and background. Then, the DPC-based constraints are applied to R-Anomaly Detector (RAD), and three band prioritization (BP) criteria are derived to obtain the representative band subset for anomalies. Experiments on two datasets show the superiority of RDBS over other BS algorithms and verify that the obtained band subsets are strongly representative of anomalies.

This article presents a new approach, called progressive band subset fusion (PBSF) for hyperspectral anomaly detection. Unlike band selection (BS) which selects bands according to band prioritization or band search strategies, PBSF fuses band subsets progressively during data collection processing. It is completely opposite to BS that must be done after data are acquired and then select bands by removing spectral redundancy as post-data processing. To accomplish PBSF, two versions of PBSF are derived: PBSF of the multiple-band subset (PBSF-MBS) and PBSF of uniform BS (PBSF-UBS). In particular, the fusion process takes place in an anomaly detector from a real-time processing perspective. Three approaches are developed to realize PBSF of two-band subsets simultaneously: PBSF-band sequential (PBSF-BSQ), PBSF-RT, and PBSF-zigzag. Extensive experiments demonstrate that PBSF has advantages over BS in many ways.

When predicting data for which limited supervised information is available, hyperspectral target detection methods based on deep transfer learning expect that the network will not require considerable retraining to generalize to unfamiliar application contexts. Meta-learning is an effective and practical framework for solving this problem in deep learning. This article proposes a new meta-learning based hyperspectral target detection using Siamese network (MLSN). First, a deep residual convolution feature embedding module is designed to embed spectral vectors into the Euclidean feature space. Then, the triplet loss is used to learn the intraclass similarity and interclass dissimilarity between spectra in embedding feature space by using the known labeled source data on the designed three-channel Siamese network for meta-training. The learned meta-knowledge is updated with the prior target spectrum through a designed two-channel Siamese network to quickly adapt to the new detection task. It should be noted that the parameters and structure of the deep residual convolution embedding modules of each channel in the Siamese network are identical. Finally, the spatial information is combined, and the detection map of the two-channel Siamese network is processed by the guiding image filtering and morphological closing operation, and a final detection result is obtained. Based on the experimental analysis of six real hyperspectral image datasets, the proposed MLSN has shown its excellent comprehensive performance.

High spectral dimensionality of hyperspectral image (HSI) has brought great redundancy for data processing. Band selection (BS), as one of the most commonly used dimension reduction (DR) techniques, attempts to remove the redundant spectral bands, while maintaining good classification or detection rate for later applications. Gray wolf optimizer (GWO) algorithm is a meta-heuristic algorithm, and it is used for HSI BS. However, the convergence factor of the basic GWO is linearly decreased, leading to a slower convergence speed and increasing the probability of falling into local optimality. This article proposes a new hybrid gray wolf optimizer (HGWO) algorithm for HSI BS, which uses adaptive decreasing convergence factor instead of linear convergence factor to improve GWO convergence rate and combines category separability for initialization to avoid local optimality. Five nonlinear functions are used to test the convergence of the proposed HGWO algorithm, compared with the state-of-the-art optimization algorithms. Finally, the experimentations are performed on three widely used real hyperspectral datasets for HSI classification, and the experimental results show that band subsets selected by the proposed HGWO algorithm can obtain better classification accuracy compared with other global optimization algorithms.

The current hyperspectral image classification (HSIC) model based on the convolutional neural network for feature extraction and softmax classifier has been prone to the barrier of label prediction with limited samples. Substituting for the enormously complicated work of terrain labeling, few-shot learning provides a popular option for HSIC with very few annotated samples. In this paper, we proposed a novel edge-inferring framework with the meta-learning paradigm for hyperspectral few-shot classification (HSFSC). In which, a graph neural network for similarity measurement is firstly presented to iteratively infer edge labels with the exploitation of instance-level similarity and the distribution-level similarity. Besides, in the meta-training stage, the pixel prediction model and patch prediction model based on edge inferring architecture are concretized jointly to improve the classification accuracy of the test samples. Expressly, at the meta-testing phase, the dynamic task-guided self-diagnosis strategy is developed for the first time to diagnose the samples separability of the current classification task, which is responsible for dynamically assigning the most reliable results based on the generated reliability grade of the sample. The extensive experimental results and analysis of three hyperspectral image datasets demonstrate the superiority of the proposed HSFSC architecture compared with other advanced methods.

Super-resolution reconstruction is a rapidly growing research area in hyperspectral data processing. However, there exist some problems, such as edge blur, burr in the smooth area, subjective design of iteration times, et al. This paper analyzes the causes of blur and burr, and puts forward some countermeasures according to these problems. Firstly, gradient interpolation is used instead of the traditional nearest neighbor interpolation, which alleviates the edge blur to a certain extent problem, the projection operator calculated from the gradient map is introduced into the projection formula to solve the burr phenomenon in the smooth area. Then, the mean square error of the reconstructed image of adjacent iterations is used to measure the similarity of the reconstructed image between two adjacent iterations, which is used as the stopping criterion of iterations, avoiding the subjectivity of setting the iteration times artificially. Finally, the proposed algorithm is applied to every band of hyperspectral image. Experimental results show that the proposed algorithm has better performance than the traditional POCS algorithm in visual effect and quantitative criteria.

This paper proposes a new hyperspectral anomaly detection method based on transferred deep learning and tensor decomposition. Firstly, since there is no labeled input data for training in anomaly detection, the detection model is obtained by training convolutional neural network with transferred learning. Then the model is decomposed to increase the number of convolution layers, that is, the depth of the network, so as to give more accurate results without over fitting. At the same time, the spatial information of the input data is extracted in order to make full use of the existing data for detection. Finally, combining the spectral and spatial information of the current pixel, the detection result is given. Experiments on two hyperspectral datasets show that the proposed algorithm has excellent performance.

This paper proposes a method to improve the performance of the supervised classification from two aspects. Firstly, the global spatial and local spectral similarity is used to extend the labeled sample size (GLS). Secondly, extended subspace projection (ESP) which projects the original image to a lower-dimensional subspace is used to alleviate band redundancy. Finally, the two implements are combined with the sparse representation classifier (SRC) to optimize the hyperspectral image classification (HSIC). The proposed method is named GLSESP. Experimental results on real hyperspectral data set demonstrate the practicality and effectiveness of GLSESP for HSIC tasks.

High dynamic range (HDR) technology is one of the most widely used ways to improve image quality, and fusion of a series of low dynamic range (LDR) images is the main measure to obtain a HDR image. However, because moving objects are often found in a series of LDR images, the fused HDR images produce ghostly shapes. In order to eliminate ghosts, this paper proposes a ghost-free multi-exposure fusion method. Firstly, aligning the moving object in the input multi-exposure sequence images with the moving object in the reference image, and the aligned sequence images are obtained. In order to consider assigning more weight to pixels in the better exposure area, two weighting functions are defined. One is to measure pixel values relative to the overall brightness and adjacent exposure images, and the other one is to reflect pixel values within a range that has a larger global gradient relative to other exposures. Based on these two weighting functions, the low exposure sequence images aligned in the Laplacian pyramid are finally fused. Through experimental comparison, the obtained image has no ghost, good visual effect, and rich details.

Wealthy spectral information provided by hyperspectral image (HSI) offers great benefits for many applications in hyperspectral data exploitation. However, processing such high-dimensional data volumes that may result in redundant bands due to its high interband correlation will be a challenge. For target detection and classification, this is particularly true since there may only need a relatively small number of bands that respond one particular target of interest well, while most of other bands do not. Band selection (BS) is a major dimensionality reduction technique to remove the redundant bands and selects a few bands to represent the entire image. However, how to eliminate the effect of uninteresting targets with similar spectra on detection of interesting targets is a severe issue arising in target detection for BS. This article develops a new approach called target-constrained interference-minimized BS (TCIMBS) which can be used to select band subset for specific target detection, while annihilating targets of no interest and suppressing interferers and background. Its idea is derived from target-constrained interference-minimized filter (TCIMF). By taking advantage of TCIMF, two band prioritization (BP) criteria called forward minimum variance BP (FMinV-BP) and backward maximum variance BP (BMaxV-BP) along with their three band search-based BS counterparts called sequential forward TCIMBS (SF-TCIMBS), sequential backward TCIMBS (SB-TCIMBS), and improved SB-TCIMBS (SB-TCIMBS*) are derived. The experimental results suggest that TCIMBS can improve the detection accuracy and also achieve better performance in comparison with several state-of-the-art methods.

Low rank and sparsity matrix decomposition (LRaSMD) has received considerable interest lately. One of effective methods is called go decomposition (GoDec) which finds low rank and sparse matrices iteratively subject to a predetermined low rank order, m and a sparsity cardinality, k, In order to resolve issue of the empirically determined m and k, the well-known virtual dimensionality (VD) and a minimax-singular value decomposition (MX-SVD) developed in maximum orthogonal complement algorithm (MOCA) are used for this purpose. The constrained energy minimization (CEM) is used for experiments to demonstrate that the GoDec with VD and MX-SVD performs very effectively.

Hyperspectral imagery provides wealthy spectral information to make it suitable for many applications. However, for specific applications, extracting suitable bands from high-dimensional data is a tedious and difficult task. In the past, many methods have been developed to perform band selection for specific tasks such as target detection. However, there is very little work to consider and deal with the effects of suspected interfering targets. In this paper, a new method for band selection, called target-constrained interference-minimized band selection (TCIMBS) is developed for specific target detection. It can select a band set with strong characterization capabilities for desired targets and good suppression for undesired targets and background (BKG). Experimental results demonstrate that TCIMBS can improve the detection performance, and also achieve better performances in comparison with several state-of-the-art methods.

This letter presents a superpixel-based dual window RX (SPDWRX) anomaly detection (AD) algorithm that uses superpixel segmentation (SPS) to adaptively determine the dual window for local RX (LRX) detection, rather than using a fixed dual window. The main premise of SPDWRX is to first divide the hyperspectral image into multiple superpixels and then extend the minimum bounding rectangle to determine the background of each superpixel. Finally, LRX AD is conducted on each pixel in the same superpixel using the same background. Furthermore, a fine SPS method is proposed based on the entropy rate superpixel to quickly obtain uniform superpixels. The experimental results show that the proposed SPDWRX method can significantly improve the detection speed and slightly improve the detection performance, and the modified SPS can further improve the detection performance of SPDWRX.

This paper presents a class information (CI)-based band selection (BS) approach to hyperspectral image classification (HSIC). It introduces a new concept from an information theory point of view, CI which can be used to determine an appropriate weight imposed on each class of interest. Specifically, two types of criteria, intraclass information criterion (IC) and interclass IC are derived as CI probabilities to measure CI that can be used to determine the number of training samples required to be selected for each class. With such CI-calculated probabilities, another new concept called class self-information (CSI) is also defined for each class that can be further used to define the class entropy (CE) so that CSI and CE can be used to determine the number of bands required for BS, nBS. In order to find desired nBS bands, two types of BS methods based on CSI and CE are custom-designed, called single class signature-constrained BS (SCSC-BS) which utilizes the constrained energy minimization (CEM) to constrain each individual class signature to select bands for a particular class according to its CSI-determined nBS and a multiple class signatures-constrained BS (MCSC-BS) which takes advantage of linearly constrained minimum variance (LCMV) to constrain all class signatures to select CE-determined nBS bands for all classes. These SCSC-BS and MCSC-BS selected bands are then used to perform classification and evaluated by CI-weighted classification measures by real image experiments. The results show that HSIC using judiciously selected partial bands as well as CI-weighted measures can improve HSIC with using full bands.

This paper develops a new approach to band selection for multiple-target detection, called constrained-target band selection (CTBS). Its idea is derived from the concept of constrained energy minimization (CEM) by constraining a target of interest, while minimizing the variance resulting from the background (BKG). By taking advantage of CEM, the variance produced by a target of interest can be further used as a measure of prioritizing bands as well as a means of selecting bands for this particular target. As a result, two CTBS-based band prioritization (BP) criteria, called minimal variance-based BP (MinV-BP) and maximal variance-based BP (MaxV-BP), and two CTBS-based BS methods, called sequential forward CTBS (SF-CTBS) and sequential backward CTBS (SB-CTBS), can be derived for multiple-target detection. Since the bands selected by CTBS vary with targets of interest used to constrain CEM, in order for CTBS to be applied to multiple targets, a new fusion technique, called band fusion selection (BFS), is further developed for CTBS to integrate bands selected by different targets so that CTBS can work for all targets. Unlike most BS methods for target detection which generally simultaneously select a fixed set of bands for all targets of interest, the ideas of constraining multiple-target detection and using BFS are novelty of this paper. Experimental results show that CTBS performs well for multiple-target detection.

In hyperspectral image classification (HSIC), background (BKG) is generally excluded from consideration due to the fact that obtaining complete knowledge of BKG is nearly impossible in reality. Unfortunately, BKG has significant impact on classification and band selection (BS). This paper investigates both issues and presents a novel approach called class signature-constrained BKG suppression (CSCBS) approach to BS for HSIC, where class signatures can be obtained either by a priori or a posteriori knowledge or training samples, and BKG suppression can be accomplished by taking the inverse of the sample correlation matrix R. Its idea takes advantage of the concept of the linearly constrained minimum variance (LCMV) developed from adaptive beamforming by constraining class signatures of interest while minimizing the effect caused by the unknown BKG so as to enhance the classification performance. There are two immediate applications of CSCBS. One is its application to HSIC, in which it becomes a CSCBS classifier. The other is its use of the LCMV-suppressed BKG as a measure to derive the band prioritization (BP) criteria and BS. Experimental results demonstrate that generally CSCBS does not need the full-band set for HSIC since a partial band subset selected by CSCBS-BP/BS can actually improve the classification results using full-band information.

Anomaly detection (AD) generally finds targets that are spectrally distinct from their surrounding neighborhoods but cannot discriminate its detected targets one from another. It cannot even perform classification because there is no prior knowledge about the data. This paper presents a new approach to AD, to be called a posteriori AD for unlabeled anomaly classification where a posteriori indicates that information obtained directly from processing data is used as new information for subsequent data processing. In particular, a posteriori AD uses a Gaussian filter to capture spatial correlation of detected anomalies as a posteriori information which is included as new information for further AD. In doing so, a posteriori AD develops an iterative version of AD, referred to as iterative anomaly detection (IAD), which implements AD by feeding back Gaussian-filtered AD maps in an iterative manner. It then uses an unsupervised target detection algorithm to identify spectrally distinct anomalies that can be used to specify particular anomaly classes. To terminate IAD, an automatic stopping rule is also derived. Finally, it uses identified distinct anomalies as desired target signatures to implement constrained energy minimization (CEM) to classify all detected anomalies into unlabeled classes. The experimental results show that a posteriori AD is indeed very effective in unlabeled anomaly classification.

Anomaly detection (AD) is designed to find targets that are spectrally distinct from their surrounding neighborhood. Unfortunately, commonly used anomaly detectors generally do not take into account its surrounding spatial information. This paper derives an iterative version of anomaly detection, iterative anomaly detection (IAD) to address this issue. Its idea is to use a Gaussian filter to capture spatial information of the anomaly detection map and then feeds back the Gaussian filtered AD map to create a new data cube. The whole process is repeated over again in an iterative manner. When IAD is terminated anomaly representatives are identified and can be used as desired target signatures to implement constrain energy minimization (CEM) so as to classify all detected anomalies. Accordingly, IAD can be considered as anomaly classification.

Automatic target generation process (ATGP) has been widely used for unsupervised hyperspectral target detection. It implements a succession of orthogonal subspace projections (OSPs) to extract targets of interest without prior knowledge. This paper extends ATGP to a kernel version of ATGP, called kernel ATGP (KATGP) to further deal with linear non-separation problem. It introduces nonlinear kernels to map original data space into a higher dimensional feature space so that ATGP can effectively find.

This paper extends target-constrained interference minimized filter (TCIMF) to multiclass-constrained background suppression classifier (MCBSC) for hyperspectral image classification. In order to capture spatial contextual information MCBSC makes use of a Gaussian filter to feed back a Gaussian-filtered MCBSC map to create a new set of hyperspectral images for MCBSC to be re-implemented again in an iterative manner, referred to as iterative MCBSC (IMCBSC). Finally, it uses Otsu's method to perform hyperspectral image classification. As shown by experiments, MCBSC generally performs better than existing spectral-spatial hyperspectral image classification techniques in terms of several quantitative measures, such as classification rate, false classification rate, precision rate, accuracy rate in addition to overall accuracy (OA) rate.

This letter develops a progressive band processing (PBP) of fast iterative pixel purity index (FIPPI) according to a band sequential acquisition format in such a way that FIPPI can be processed band by band, while band acquisition is ongoing. As a result, PBP-FIPPI can generate progressive profiles of interband changes among PPI counts which allow users to observe significant bands that capture PPI counts. The idea to implement PBP-FIPPI is to use an inner loop specified by skewers and an outer loop specified by bands to process FIPPI. Interestingly, these two loops can also be interchanged with an inner loop specified by bands and an outer loop iterated by growing skewers. The resulting FIPPI is called progressive skewer processing of FIPPI. It turns out that both versions provide different insights into the design of FIPPI.

This paper presents a new approach, called band subset selection (BSS)-based hyperspectral anomaly detection (AD), which selects multiple bands simultaneously as a band subset rather than selecting multiple bands one at a time as the tradition band selection (BS) does, referred to as sequential multiple BS (SQMBS). Its idea is to first use virtual dimensionality (VD) to determine the number of multiple bands, nBS needed to be selected as a band subset and then develop two iterative process, sequential BSS (SQ-BSS) algorithm and successive BSS (SC-BSS) algorithm to find an optimal band subset numerically among all possible nBS combinations out of the full band set. In order to terminate the search process the averaged least-squares error (ALSE) and 3-D receiver operating characteristic (3D ROC) curves are used as stopping criteria to evaluate performance relative to AD using the full band set. Experimental results demonstrate that BSS generally performs better background suppression while maintaining target detection capability compared to target detection using full band information.

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral-spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu's method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates.

Taking full advantage of nonlinear information, kernel-based nonlinear versions of anomaly detection algorithms generally gain wide attention in hyperspectral imagery. Kernel RX algorithm is not new but a real-time procedure of KRX has not been explored in the past. The need of real-time processing arises from the fact that many targets especially moving targets, must be detected on a timely basis. This paper presents a real-time anomaly detection algorithm based on KRX, named as real-time causal kernel RX detector (RTCKRXD) by which hyperspectral image data can be processed timely. Experimental results demonstrate the new real-time version of KRX significantly solves real-time processing problem compared to conventional KRX anomaly detector with a comparable detection performance.

Constrained energy minimization (CEM) has been widely used for subpixel detection. It takes advantage of inverting the global sample correlation matrix R to suppress background so as to enhance detection of targets of interest. This paper presents a progressive band processing of CEM (PBP-CEM) which can perform CEM for target detection progressively band by band according to band sequential format. In doing so, a new concept, called causal band correlation matrix (CBCM), is introduced to replace the global sample correlation matrix R. It is a global correlation matrix formed by only those bands that were already visited up to the band currently being processed while excluding bands yet to be visited in the future. The proposed PBP-CEM allows CEM to be processed whenever bands are available, without waiting for completing band collection. With such an advantage, CEM has potential in data transmission and communication, specifically in satellite data processing.

Anomaly detection using sliding windows is not new but using sliding causal windows has not been explored in the past. The need of causality arises from real time processing where the used sliding windows should not include future data samples that have not been visited, i.e., data samples come in after the currently being processed data sample. This paper develops an approach to anomaly detection using sliding causal windows that has capability of being implemented in real time. In doing so two types of causal windows are defined, causal matrix window and causal array window from which a causal sample covariance/correlation matrix can be derived. As for the causal array window recursive update equations are also derived and thus, speed up real time processing.