Deep learning-based methods have demonstrated promising performance in hyperspectral image (HSI) classification. However, the black-box nature of deep learning poses a significant challenge in designing effective network architectures for HSI classification. To overcome this issue, this article presents a representation model-inspired interpretable deep unfolding network (NSR-Net). First, we formulate a deep-constrained nonnegative sparse representation (NSR) model with enhanced generalization ability to address the limitations of the prior-constrained NSR, i.e., its reliance on manual priors and specific assumptions. Second, the solving process for deep-constrained NSR is unfolded into a deep network, with each component of the network corresponding directly to a specific step. Finally, following the principle of representation model-based classification, a subdictionary reconstruction module (SDRM) is designed to determine the class label. In SDRM, each subdictionary is learned through a context-integrated training process, resulting in superior discriminative capability. In addition, to better guide NSR-Net optimization, we introduce a new composite loss function, which consists of constraint loss and residual loss, aiming to effectively recover representation coefficients and reconstruct data from the subdictionary. Experiments conducted on four distinct HSI datasets illustrate the superiority and generalization performance of the proposed method compared with advanced representation model-based and deep learning-based methods, with overall accuracy (OA) improvements of 0.72%–9.80%, 1.39%–8.09%, 0.40%–5.85%, and 0.56%–6.84% for Indian Pines, Salinas, LongKou, and Loukia, respectively. The source code will be available at:https://github.com/ZhaohuiXue/NSR-Net.

Spatiotemporal fusion of remote sensing data is one of the critical techniques for Earth’s surface dynamic monitoring and analysis, which solves the limitation of spatial resolution and temporal coverage in individual sensor. In order to establish a more accurate and physically meaningful spatiotemporal fusion model, a weighted spatiotemporal fusion method via tensor collaborative representation (W-STFTCR) is proposed. Specifically, the collaborative representation (CR) constraint is incorporated into the tensor decomposition framework to prevent overfitting and enhance model robustness. Meanwhile, the superpixel segmentation strategy is adopted to partition the input difference image into superpixel blocks, facilitating block dictionary construction and clustering effectively. In addition, the normalized difference vegetation index (NDVI) and joint information entropy are introduced for weighting bands in predicting the final image, which leads to more accurate and physically meaningful outcomes. To verify the performance of the proposed method, the spatiotemporal fusion experiments on two publicly available datasets were conducted. The experiment results show that the proposed method outperforms the previous state-of-the-art (SOTA) spatiotemporal fusion algorithms, with excellent parameter robustness.

Hyperspectral images capture numerous narrow spectral bands to provide detailed information to identify and locate targets, making them highly suitable for anomaly detection tasks. In recent years, deep learning techniques have demonstrated impressive capabilities and prospects in hyperspectral anomaly detection (HAD), primarily relying on multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) to extract and represent nonlinear features. However, MLPs and CNNs often require deeper network architectures when dealing with complex high-dimensional data, resulting in a constrained generalization and limited representation of features. To address this issue, and inspired by the recent Kolmogorov-Arnold network (KAN), this article introduces a novel asymmetric convolutional autoencoder (AE) network by integrating KAN and CNN, namedKACNet. Specifically, we design a spectral KAN block in the convolutional encoder and a spatial KAN block in the convolutional decoder, to simultaneously enhance the feature extraction and characterization capabilities of the network. Furthermore, to effectively utilize the limited prior information, a weight initialization mechanism based on hierarchical density-based spatial clustering of applications with noise (HDBSCAN) is developed to boost the background recovery. By combining KAN, CNN, and HDBSCAN, the proposed integration enhances the interpretability and reliability of HAD. Extensive experiments are conducted on six public datasets, demonstrating that the KAN poses remarkable performance on background reconstruction, particularly, the proposedKACNetsignificantly outperforms the other state-of-the-art methods.

Hyperspectral image classification (HSIC) often struggles due to the scarcity of labeled samples. Knowledge distillation (KD), including self-distillation (SD) where a model learns from its own predictions, has emerged as a promising solution. However, existing distillation methods in HSIC face a “granularity mismatch” problem as they rely on coarse, patch-level data for fine-grained, pixel-level classification, which introduces label noise and causes misclassification. To overcome this issue, we propose central spectral self-distillation (CSSD), a framework that isolates pure spectral information at the patch center and leverages it for SD. CSSD consists of three main components. First, the backbone network separates spectral and spatial feature processing to extract pure central spectral features. Second, a spectral refiner module enhances these spectral features before integrating spatial context. Finally, an SD loss aligns the final predictions with the central spectral guidance, ensuring granularity matching at the pixel level. The experimental results on five hyperspectral datasets demonstrate the effectiveness of CSSD under few-shot conditions. The source code will be available online athttps://github.com/ZhaohuiXue/CSSD.

Transformer-based deep learning (DL) methods have gradually been advocated for remote sensing (RS) image semantic segmentation due to the great global modeling capability. Nevertheless, Transformer-based DL methods have not yet been sufficiently explored on the large-scale hyperspectral image (HSI) semantic segmentation. Current algorithms lack a comprehensive consideration of the impact of positional encoding (PE) interpolation when constructing Transformer-based decoders. Moreover, existing segmentation heads usually directly concatenate multiscale features to achieve segmentation, which ignores the inherent semantic differences between different features. To address the above issues, a U-shaped multimixed Transformer network (UM2Former) is proposed for large-scale HSI semantic segmentation. First, a weight encoder consisting of two modules, the overlap-down and the channel-weight, is built to extract hierarchical discriminative spectral-spatial features and decrease spectral redundancy. Second, the proposed multimixed Transformer block (MMTB) develops a PE-free module, spatial-feature-retention attention (SFRA) mechanism, in which “multimixed” represents the global dependency modeling of each pixel with the retented average spatial characteristics of different locations in the input feature maps. Finally, a linear fuse segmentation head (LFSH) is designed to align semantic information among multiscale feature maps and achieve accurate segmentation. Experiments were conducted in single cities and the entire large-scale WHU-OHS HSI dataset. The segmentation results indicated that the proposed method achieved higher accuracy compared to the existing semantic segmentation methods, with performance improvements of 17.80% and 4.16% in terms of intersection over union (mIoU) and overall accuracy (OA), respectively. The source code will be available athttps://github.com/ZhaohuiXue/UM2Former.

A novel approach is proposed in this study that combines superpixel (SP) segmentation and multi-classifier ensemble learning to address the limited availability of labeled samples in the coastal wetland land cover classification. Firstly, the SP segmentation techniques is employed to partition unknown samples into multiple homogeneous regions, thereby facilitating the effective capture of spatial information pertaining to land cover. Subsequently, a multiclassifier ensemble learning strategy is employed within these regions to process the samples, effectively leading to a reduction in classification errors and an improvement in accuracy. To enhance the performance of semi-supervised learning (SSL), a sample iteration selection metric is introduced, optimizing the training samples based on the consistency of sample types within homogeneous regions and the results obtained from the multi-classifier ensemble, thus enhancing the reliability of pseudo-labels. Additionally, multi-scale SP segmentation is utilized to augment the ensemble strategy for samples, reducing the necessity for hyperparameter adjustments and increasing the automation and reliability of the model. The effectiveness of the proposed approach has been assessed through experiments conducted on Dafeng Natural Reserve hyperspectral images of wetlands in China.

Hyperspectral sensors carried by lunar rovers or satellites can effectively invert the mineral abundance of the lunar surface. Due to special environment of the lunar surface and small number of samples, it is a challenge to analyze typical minerals on the lunar surface using hyperspectral images. In this paper, a spectral-spatial diffusion autoencoder unmixing model (SSDiffAU) is proposed for mineral mapping. This is the first time the diffusion model is invoked in the unmixing field. The 3D-CNN is utilized as an encoder to represent deep spectral-spatial information. Additionally, the diffusion model is used to obtain high quality abundance maps, and then the endmember matrix is obtained by the decoder. Finally, the performance of the proposed algorithm was verified using popular unmixing datasets. The hyperspectral data acquired by Chang'e-3 Yutu rover are unmixed to estimate typical mineral spectra and their abundance.

Unlike other deep learning (DL) models, Transformer has the ability to extract long-range dependency features from hyperspectral image (HSI) data. Masked autoencoder (MAE), which is based on Transformer architecture, employs a “mask-reconstruction” strategy for training, allowing the model to be effective for downstream tasks. However, existing MAE-based methods only apply spectral or spatial masking to HSI and reconstruct them for feature learning, which is too simplistic and insufficient for the model to learn robust features. Additionally, the issue of lacking labeled samples in HSI and the primary objective of MAE to reduce the reliance on labeled samples are often overlooked. To address these issues, we are inspired by diffusion-based representation learning and propose diffusion-enhanced MAE (DEMAE) for HSI classification with few labeled samples. First, an asymmetric encoder–decoder framework is constructed as the backbone by stacking both conditional and standard Transformer blocks. Second, we devise an auxiliary task aimed at simultaneous denoising and reconstruction, facilitating heuristic feature learning from HSI data. Third, the encoder of DEMAE is isolated for training with few labeled samples. Finally, the encoder is used for classification, and a novel signal-to-noise ratio enhanced (SNR-Enhanced) loss function is introduced to regularize the model training process. The performance of DEMAE is evaluated on four benchmark datasets, demonstrating its superiority in classification accuracy and mapping capabilities on unlabeled areas compared to existing state-of-the-art methods with few labeled samples. The source code will be available online athttps://github.com/ZhaohuiXue/DEMAE.

Hyperspectral coastal wetland classification requires an extensive quantity of labeled samples, which are hard to acquire. Therefore, a novel semi-supervised dynamic ensemble learning (SSDEL) framework is proposed to overcome the limitations of labeled samples in wetland hyperspectral classification. Firstly, a collaborative relationship is established between labeled and unlabeled samples in the sample augmentation stage. Based on this relationship, unlabeled samples were assigned to the region to which the most similar samples belonged. Then, multiple classifiers are trained using labeled samples and predict unlabeled samples in the same region to obtain higher confidence pseudo-label results. Secondly, based on the assumption that different classifiers should produce similar classification results for a specific target sample, an objective function is designed to unify the classification behavior of multiple classifiers. The representation coefficients of multiple classifiers in the same region are constrained by optimizing the objective function through thel2norm. Finally, a complete SSDEL framework is constructed by applying consistency learning again to the augmented samples. The proposed method is evaluated using three wetland hyperspectral images of China, and the experiments results demonstrate its effectiveness.

Low-rank and sparse representation (LRSR) model has gained popularity in hyperspectral image (HSI) classification. However, most existing LRSR models are limited by the highly nonlinear correlation of hyperspectral data, which leads to poor subspace segmentation performance. Furthermore, current LRSR methods usually directly used labeled samples to build the dictionary, whereas low discriminative labeled samples may degrade the representation ability of the dictionary. To solve the above issues, we propose a novel structure-prior-constrained low-rank and sparse representation with discriminative incremental dictionary (SPCLSR-DID) method for HSI classification. First, global and local data structures are maintained by low-rank and sparsity constraints, while a structural prior constraint is introduced to explore the intrinsic spectral-spatial structural information of HSI, improving the subspace segmentation ability of the model. Second, a discriminative incremental dictionary (DID) method is presented to find reliable and discriminative augmented atoms to improve the completeness and representation power of the dictionary. In DID, the incremental dictionary size is controllable to suit different tasks. Finally, the class label of each target sample is determined by jointly considering contextual information within a certain local range, which ensures the accuracy and smoothness of the classification map. Experimental results based on four popular hyperspectral datasets demonstrate that the proposed SPCLSR-DID method significantly outperforms other related comparison methods in terms of classification accuracy and generalization performance.

A novel approach is proposed in this study that combines superpixel (SP) segmentation and multiclassifier ensemble learning (EL) to address the limited availability of labeled samples in coastal wetland land cover classification. First, the SP segmentation technique is employed to partition unknown samples into multiple homogeneous regions, thereby facilitating the effective capture of spatial information pertaining to land cover. Subsequently, a multiclassifier EL strategy is employed within these regions to process the samples, effectively leading to a reduction in classification errors and an improvement in accuracy. To enhance the performance of semi-supervised learning (SSL), a sample iteration selection metric is introduced to optimize the training samples based on the consistency of sample types within homogeneous regions and the results obtained from the multiclassifier ensemble, thus enhancing the reliability of pseudo-labels. Additionally, multiscale SP segmentation is utilized to augment the ensemble strategy for samples in order to reduce the necessity for hyperparameter adjustments and increase the automation and reliability of the model. Overall, the accuracy of coastal wetland classification is improved by this approach while simultaneously mitigating the complexity of SSL in terms of hyperparameter tuning. The effectiveness of the proposed approach has been assessed through experiments conducted on three GF-5 hyperspectral images of coastal wetlands in China. In particular, the proposed methods provide superior performance compared with the state-of-the-art classification methods.

Graph convolutional network (GCN) combined with convolutional neural network (CNN) exhibits significant potential in hyperspectral image (HSI) classification. Hypergraph convolutional network (HGCN) can address the limitations of GCN-based methods in representing high-order nonlinear relationships among multiple nodes. However, the existing pixel-based HGCN methods mostly adopt partial pixels for hypergraph modeling, thus limiting the representation of the global structure. Additionally, both pixel-based and superpixel-based HGCN methods only utilize the k-nearest neighbors (kNN) for hyperedge representation, thereby ignoring the rich topological information in HSI segmentation regions. To tackle these issues, we propose an HGCN with multiple hyperedges fusion (HGCN-MHF) for HSI classification with limited samples. First, we introduce a CNN branch for capturing spatial and spectral pixel-level features with different receptive fields, which begins with denoising and spectral transformation, followed by cross multiscale convolution (CMC). Second, we design an HGCN branch for extracting superpixel-level features guided by diversified high-order hypergraph structures, which incorporates a multiple hyperedges fusion (MHF) module followed by hypergraph convolution (HGC). Finally, a score-weighted feature fusion (SWFF) strategy is proposed to balance and promote the feature fusion of the two branches. Experimental results on four benchmark HSI datasets demonstrate that HGCN-MHF outperforms other state-of-the-art methods, with improvements in terms of overall accuracy (OA) around 3.50%–20.47% (Indian Pines), 2.85%–19.83% (University of Pavia), 2.31%–8.75% (Salinas), and 3.13%–20.03% (WHU-Hi-HongHu) under five labeled samples per class.

Hyperspectral anomaly detection, which aims to search and localize potential targets, is a research area with extensive application prospects and profound implications. In recent years, the emergence of unsupervised and self-supervised deep learning for image reconstruction has provided inspiring solutions for hyperspectral anomaly detection. However, due to sensor-induced and environmental effects, the full-image detection networks inevitably reconstruct anomalies along with the background. Existing detectors indirectly mitigate anomaly reconstruction by imposing constraints on hidden features or loss functions, but they provide unsatisfactory performance in large target detection scenarios. This work straightforwardly addresses this issue from the input source, i.e., introducing the concept of masked autoencoders (MAEs) into fully convolutional networks and further developing a sparse-inspired masked convolutional network (SMCNet) consisting of three mutually supportive components: 1) a hierarchical encoder; 2) a sparse projection layer; and 3) a hierarchical decoder. The encoder employs an adaptive potential anomaly masking strategy, leveraging sparse convolution for extracting multidimensional features of the remaining background. Meanwhile, a sparse-guided projection layer is created by discarding the positional embedding technique to populate the uncoded region and guide the background recovery without introducing anomalies. Finally, the decoder couples the hierarchical structure and a hybrid attention mechanism (local-middle–global and spatial-spectral) to refine the background during image recovery, whereas anomalies in the residual map are highlighted. Extensive experiments using ten typical competitors on six different types of datasets validate the effectiveness and generalization ability of the newly proposed SMCNet method.

Spectral shifts between source and target domains (TDs) pose significant challenges in cross-domain hyperspectral image classification (HSIC). Current methods often struggle to balance mitigating these shifts while preserving crucial TD information, which limits their ability to leverage spectral priors and domain-specific characteristics for accurate classification. Our work proposes a novel knowledge swap net (KSN) for few-shot cross-domain HSIC. KSN tackles the challenge by enabling effective knowledge transfer between homogeneous (spectral) and heterogeneous (domain-specific) feature spaces through a two-step knowledge swap strategy: leveraging homogeneous knowledge distillation (Homo-KD) for transferring spectral knowledge and heterogeneous meta-learning (Hetero-ML) for model refinement with TD feedback. In addition, we develop a relative distance difference (RDD) loss function to improve feature discriminability under few-shot conditions. Experiments conducted on four target datasets demonstrate the superiority of KSN. Notably, KSN achieves a remarkable overall accuracy (OA) of 82.56% on the Houston University (HU) 2013 dataset, surpassing other leading methods by 3.83%–8.98%. The source code will be available online:https://github.com/ZhaohuiXue/KSN.

In recent years, hyperspectral anomaly detection methods based on representation models has attracted much attention. However, when the dictionary is polluted by anomalous pixels, their performance is greatly affected. To adjust the contributions of different dictionary atoms, traditional methods usually predefine a distance weighting matrix and impose it on the dictionary matrix or coefficient vector, which may not be accurate enough. To solve this problem, a self-paced probabilistic collaborative representation detector (SP-ProCRD) is proposed in this article. It assigns weights for each atom loss term according to the probability that the pixel under test (PUT) belongs to the same class as each dictionary atom. Unlike the predefined weight matrix approach, a self-paced learning (SPL) strategy is used for iterative optimization, so that dictionary atoms participate in the representation from "good" to "bad" ones when solving the model. The representation residuals are utilized to accelerate the convergence. The proposed model can optimally represent each PUT using similar dictionary atoms and minimize the negative impact caused by anomalous atoms contained in the dictionary. In terms of weighting for SPL, an adaptive weighting scheme based on the polynomial self-paced (SP) regularizer is proposed to address the generalization issues of most previous weighting schemes. This scheme improves the generalization and automation of the model. Experimental results reveal that the proposed method produces more accurate result than existing methods and runs efficiently.

Graph convolutional networks (GCNs) have been skillfully employed in hyperspectral image (HSI) classification, exhibiting remarkable performance owing to their unique superiority in handling non-Euclidean graph-structured data. However, the inherent absence of predefined connections between pixels in HSI results in the underutilization of the structural and attribute information of the graph edges. Furthermore, the construction of adjacency matrices for large-scale HSI data imposes a huge computational burden on traditional GCNs. Therefore, in this article, a novel method combining relaxed collaborative representation (RCR) and GCN (RCR-GCN) for hyperspectral classification is proposed. Specifically, RCR is adopted to compute the representation coefficients of each feature, reflecting the similarity and diversity among different sample features. Meanwhile, the representation coefficients are applied as edge attributes in the graph, denoting the weights of the connections between neighboring nodes. After that, GCN is employed to classify the graph nodes. Moreover, an efficient version of the RCR-GCN method is developed to boost the computation, which constructs the graph based on superpixel nodes instead of the pixel nodes by using simple linear iterative clustering (SLIC). Extensive experiments on three HSI image datasets demonstrate that the proposed method outperforms other state-of-the-art methods and achieves more efficiency and feasibility in HSI image classification.

Wetland contains numerous features, and label acquisition is time-consuming, laborious, and inaccurate. Coastal wetland land cover classification with limited labeled training samples has become a significant challenge. In this study, a novel probabilistic ensemble sample selection framework (ProESS) is proposed for coastal wetland land cover classification. First, a sample probabilistic confidence index (SPCI) is proposed, which is defined by probabilistic output of each base classifier. Then the prediction confidences of unknown samples are obtained by joint probability of ensemble base classifiers, which can select high-quality samples to improve classification performance. However, the low accuracy of base classifiers will affect the confidence of samples selected by SPCI, thus reducing the classification accuracy. Based on this observation, an adaptive representation coefficient consistency learning (AdaRCCL) is proposed to help define SPCI. Finally, a ProESS is constructed through SPCI and AdaRCCL which can obtain training samples with high confidence from unknown samples. To evaluate the effectiveness of proposed methods, the three wetland hyperspectral datasets of China, i.e., Yangtze River Delta, Jiangsu Dafeng Natural Reserve, and Yellow River Delta, are used for classification experiments in the paper. Experimental results show that the proposed algorithms achieve higher performance and are robust to parameters in comparison to the baseline and the state-of-the-art ensemble algorithms. The extensibility and transferability of proposed methods are also discussed in the paper. Better results on multiple machine learning models with new samples show the extensibility of SPCI and ProESS. The great performance on Botswana dataset also demonstrates the transferability of proposed methods.

Protection of wetlands is important for ecosystem in recent years, and the classification of wetland ground cover is the foundation of investigation and protection work. Probabilistic collaborative representation classifier (ProCRC) is one of the best performing classifiers which has been applied in hyperspectral image (HSI) classification. However, its performance is greatly limited for wetland data where spectrums are highly similar. Moreover, the complex distribution of ground objects in wetlands have not been wisely utilized in the classification. In this article the intrinsic mechanism of ProCRC is found and its kernel version is proposed to solve the problems of wetlands classification. Then, a new ensemble learning strategy that considers neighborhood information are proposed, which largely alleviates the problem of sample collection in wetlands. Under the guidance of this strategy, two specific ensemble learning algorithms, i.e., LNE and LNSAE, are proposed. The superiority of proposed methods is validated using three typical HSI data sets of China coastal wetland with few samples.

Autoencoder-based hyperspectral anomaly detectors have received significant attention. The core of these detectors is to reconstruct backgrounds by optimizing autoencoders so that anomalies can be detected by reconstruction residuals. Nevertheless, existing methods are flawed in two aspects: 1) most of them reconstruct the background along with the anomalies, resulting in undesired performance for large target detection in complex backgrounds; 2) they only focus on the encoder optimization part, ignoring the decoder reconstruction quality of the background. Given the above, this paper proposes a background-guided deformable convolutional autoencoder (DCAE) network with three mutually supportive parts, including encoder, decoder, and background guidance modules. In the encoder, deformable convolution is introduced into regular convolution to build the adaptive spatial feature extractor to fit complex spatial structures, whilst a non-local convolution is introduced to build an external feature extractor to capture global spatial relationships. Further, a mask is designed to filter potential anomalous information, curbing the representation of high-frequency anomalies to focus on widespread backgrounds. In the decoder, a background guidance module (considering the physical meaning of linear reconstruction) is built, guiding the proposed network learning via two strategies. One is initializing the weight of the decoder, and another is adding a loss term. Notably, both the number of output channels of the encoder and the decoder construction are determined by the background guidance module, which creates a bridge between the network design and practical situations. A profound analysis demonstrates the outstanding performance of the proposed method, which outperforms traditional and deep learning methods, proving that the novel designs introduced in the network architecture are extremely effective.

Hyperspectral unmixing with tensor models has received great attention in recent years. A tensor-based decomposition method can effectively represent the structural feature of hyperspectral images; however, the obtained results may be physically uninterpretable. To overcome this limitation, a novel adaptive hypergraph regularized multilayer sparse tensor factorization (AHGMLSTF) algorithm is proposed. First, a modified hypergraph is incorporated into tensor factorization, and the modified hypergraph uses spectral angle distance (SAD) instead of Euclidean distance to construct hyperedges to better represent the joint spatial and spectral information. Then, the hypergraph is constructed adaptively by hyperedges of$k$neighborhoods. Second, the concept of multilayer decomposition is introduced to explore the hierarchical features of hyperspectral images, and a sparse constraint is imposed on each layer to make the unmixing results more consistent with the physical mechanism of mixed spectral pixels. With these constraints, the proposed method established a spectral–spatial joint tensor decomposition model that represents not only the local neighborhood similarity but also the heterogeneity of adjacent edges. Experiments on simulated data and real hyperspectral data demonstrate the effectiveness of the proposed method.

The performance of hyperspectral anomaly detection based on representation models is importantly related to the corresponding dictionary. A good dictionary can optimally model background to detect anomalies. To realize adaptively background reconstruction, this paper constructs global-local dictionaries for collaborative representation detector by using adaptive-shape (SA-CRD). Specifically, robust principal component analysis (RPCA) is used to separate background and anomalies preliminarily. Then adaptive-shape neighbor is adopted to build local dictionaries for robust background region, and the robust background region is clustered to construct a global dictionary for potential anomaly region. Finally, global-local dictionaries are used in the collaborative representation model to finish anomaly detection. Obtained results over two real data sets indicate that the proposed method can improve the accuracy of anomaly detection intensively compared to other state-of-art methods.

Supervised dictionary learning and representation learning framework has demonstrated its superiority for hyperspectral image classification. Relaxed collaborative representation (RCR) has also been acknowledged as an effective method in balancing the similarity and difference between features. In this paper, a new dictionary learning method is introduced to balance discrimination and reconstruction of training samples. In the dictionary learning stage, two new indicators are designed to measure the discriminability of items and can be improved by optimizing coding coefficients. The imposedl2-norm between independent item and the mean of class-specific samples constrains the similarity, and the calculated weights measure the difference. In label determination stage, considering that the residuals of RCR are adversely affected by the significant difference of features, a new classification approach is introduced. Class labels are assigned without calculating the reconstruction errors but calculating the levels of comprehensive contribution from all training samples instead. Since the effectiveness will be degraded when dealing with more complex circumstances, a region-based version is further introduced. It can further improve the discrimination of dictionary items due to the reduced categories in each sub-image and reduce the computation cost. The experimental results on several hyperspectral datasets demonstrate that our methods can effectively improve the classification performance.

Representation learning methods, such as sparse representation (SR) and collaborative representation (CR), have been widely used in hyperspectral image classification. However, they merely considered the similarities between features. Due to the plentiful spatial and spectral information in hyperspectral images, the differences between features also need to be considered. Relaxed CR (RCR) is used in face recognition to accommodate the difference and similarity of features simultaneously. In this article, a novel method of RCR with band weighting based on superpixel segmentation is proposed for hyperspectral image classification. The$\boldsymbol {l}_{ \boldsymbol {2}}$norm on band coefficients and global average coefficients is exploited to ensure the similarity, and the variance determines the specific coefficient-related weight of each band. The training set is selected from each superpixel, which is considered as a subgraph rather than independent pixels. It is favorable for concentrating on the difference between similar bands since the samples in each superpixel are of high similarity. Furthermore, extended multiattribute profile (EMAP) features, Gabor features, and local binary pattern (LBP) features are employed to increase the diversity of features; thus, a method of multifeatures’ RCR based on superpixels is proposed. Three typical data are used to validate the related algorithms. The experiments demonstrate that the proposed algorithms can effectively improve classification accuracy compared to state-of-the-art classifiers.

Anomaly detection has become an important remote sensing application due to the abundant spectral and spatial information contained in hyperspectral images. Recently, hyperspectral anomaly detection methods based on collaborative representation model have attracted significant attention. Nevertheless, these methods have to face two main challenges: (1) all features (spectral signatures) are constrained to share the same representation coefficient, which ignores the differences among features; (2) existing dictionaries for pixel-by-pixel detection model are usually not reliable. To address these issues, this paper proposes a new relaxed collaborative representation detector for hyperspectral anomaly detection by using a novel non-global dictionary. The proposed detector conducts collaborative representation on each feature dimension of the pixel under test, and simultaneously constrains the coding vectors of different features to be similar. To the best of our knowledge, this is the first time that a detection model is built from each feature dimension. To adjust the contributions of each feature, an adaptive feature weight constrained version of the method is also proposed. The non-global dictionary is constructed by combining the k-nearest neighbor method and an existing global dictionary, which is more reliable and practical than the widely used dual windows dictionary. In addition, this paper also designs a band selection strategy for the proposed method. Experiments on five real datasets indicate that the proposed method suppresses background well and outperforms other classical and state-of-the-art methods.

Recently, collaborative representation classification (CRC) has attracted extensive interest for hyperspectral images (HSIs) classification. However, for collaborative representation with Tikhonov (CRT), a testing sample is collaboratively represented by training samples from all the classes, which may result in high computational cost. In this article, we select the first$k$class training samples that are nearest to the testing sample for representation, namely,$k$-nearest class CRT (KNCCRT) algorithm. In order to improve the performance of KNCCRT for HSI classification, the idea of random subspace-based KNCCRT ensemble framework is proposed. KNCCRT is adopted as base classifier and random subspace (RS) contributes to diversity by selecting feature randomly. Moreover, to further increase the classification accuracy, shape-adaptive (SA) neighborhood constraint is utilized in RS ensemble framework to incorporate spatial information. Experimental results on three real hyperspectral data sets demonstrate the effectiveness of the proposed methods for HSI classification. The combination of KNCCRT and RS framework provides a reliable accuracy for HSI classification.

Recently, collaborative representation classification (CRC) has attracted much attention for hyperspectral image analysis. In particular, tangent space CRC (TCRC) has achieved excellent performance for hyperspectral image classification in a simplified tangent space. In this article, novel Bagging-based TCRC (TCRC-bagging) and Boosting-based TCRC (TCRC-boosting) methods are proposed. The main idea of TCRC-bagging is to generate diverse TCRC classification results using the bootstrap sample method, which can enhance the accuracy and diversity of a single classifier simultaneously. For TCRC-boosting, it can provide the most informative training samples by changing their distributions dynamically for each base TCRC learner. The effectiveness of the proposed methods is validated using three real hyperspectral data sets. The experimental results show that both TCRC-bagging and TCRC-boosting outperform their single classifier counterpart. In particular, the TCRC-boosting provides superior performance compared with the TCRC-bagging.

Recently, low-rank representation and collaborative representation for hyperspectral anomaly detection are widely studied. In this paper, a novel anomaly detector which combines low-rank and collaborative representations for hyperspectral anomaly detection (LRCRD) is proposed. Different from existing anomaly detection methods using low-rank and collaborative representation, the proposed method divides an image into two parts: background and anomaly targets. A background dictionary is used to represent the background whose coefficient matrix is constrained by low-rank and l2norm minimization. The sparsely distributed anomalies are determined by the residual matrix which is constrained by l2,1norm minimization. Considering different similarities between a testing pixel and a dictionary atom, a distance-weighted matrix is adopted. Moreover, construction of the background dictionary avoids the pollution of abnormal pixels and makes the detection result more stable. Experimental results show that the LRCRD performs better than state-of-the-art anomaly detection methods.

Collaborative representation classification (CRC) is sensitive to the regularization parameter. In order to overcome this drawback, we propose a novel Bagging-based collaborative representation classification (Bags CRC) for hyperspectral image classification, which combines CRC and Bagging together. The principal idea of Bags CRC is to generate diverse CRC classification results using bootstrape sample method, which can enhance single classifier accuracy and diversity simultaneously. In addition, tangent collaborative representation classification (TCRC) has demonstrated its better performance than that of CRC. Here, TCRC is adopted as base classifier in Bagging framework, then the Bagging-based TCRC (Bags TCRC) is proposed. The effectiveness of the proposed method is investigated on two real hyperspectral data sets. The experimental results show that both Bags CRC and Bags TCRC outperform their single classifier counterpart, respectively. Bags TCRC provides the superior performance compare with Bags CRC and random forest.

Spatial information has widely been used in hyperspectral image (HSI) classification to improve classification accuracy. However, the structural information may not be fully explored when using spatial information, this paper proposes the joint collaborative representation classification with correlation matrix (CRC-CM) for HSI by using spatial correlation features in patches, which could keep the local intrinsic structure in band images. Considering spatial heterogeneity in a patch, local correlation matrices of a target neighborhood patch and training neighborhood patch are improved by a binary weight matrix and shape-adaptive neighborhood. To explore nonlinear nature of spatial features, corresponding kernel CRC-CM is also proposed. To evaluate the effectiveness of the proposed methods, three real HSIs with different degree of heterogeneity are used. The experimental results show that the proposed spatial correlation features outperform the original spectral feature and other spatial features which widely used in HSI classifiers.

Recently, multifeature learning in collaborative representation classification (CRC) for hyperspectral images has generated promising performance. In this paper, two novel multifeature learning algorithms that update dictionary directly and indirectly are proposed. In order to offer the complementarity of multifeature, four different types of features-global feature (i.e., Gabor feature), local feature (i.e., local binary pattern), shape feature (i.e., extended multiattribute profiles), and spectral feature-are adopted in this paper. Under the hypothesis that most of the features should share the same coding pattern in CRC, this paper proposes to learn proper dictionaries for each feature until obtaining stable codes in a linear classifier. Furthermore, to avoid the explicit mapping of infinite-dimensional dictionaries in a nonlinear kernelized classifier, an indirect approach to construct the transformation matrix from original dictionaries to learn new dictionaries is developed. Three real hyperspectral images acquired from different sensors are adopted for performance evaluation. The experimental results demonstrate that the proposed methods can provide superior performance compared with those of the state-of-the-art classifiers.

Regularization has appeared explicitly in hyperspectral image (HSI) classification community, which serves as a promising paradigm for leveraging labeled and unlabeled information, computer's automation and user's interaction, spectral and spatial information, and so on. Graph-based regularization is capable of modeling the nonlinear structures embedded in high-dimensional space, with the great potential for HSI classification. However, traditional methods exhibit low capacity when facing noisy and large-scale data, thus posing a big challenge for their successful use in this community. In this paper, we present two novel sparse graph regularization methods, SGR and SGR with total variation (TV-SGR). In SGR, the labels of large unknown data are propagated based on the fraction matrix and the prediction function, where the fraction matrix is obtained using an effective sparse representation (SR) algorithm with respect to the dictionary, and the prediction function is estimated by optimizing a typical graph-based regularization problem. In contrast, TV-SGR is an extension of SGR by considering spatial information modeled by total variation in SR. Propagating the prediction function from dictionary to large unknown data using the fraction matrix is the essence of the paradigm. SGR and TV-SGR can be equipped with semisupervised learning, active learning, and spectral-spatial classification with large flexibility. The experimental results with two popular hyperspectral data sets indicate that the proposed methods outperform some state-of-the-art approaches in terms of computational efficacy, classification accuracy, and robustness to noise.

In this research, a novel sparse graph regularization (SGR) method was presented, aiming at robust crop mapping using hyperspectral imagery with very few in situ data. The core of SGR lies in propagating labels from known data to unknown, which is triggered by: 1) the fraction matrix generated for the large unknown data by using an effective sparse representation algorithm with respect to the few training data serving as the dictionary; 2) the prediction function estimated for the few training data by formulating a regularization model based on sparse graph. Then, the labels of large unknown data can be obtained by maximizing the posterior probability distribution based on the two ingredients. The study area is located at Zhangye oasis in the middle reaches of Heihe watershed, Gansu, China, where eight crop types were mapped with Compact Airborne Spectrographic Imager (CASI) and Shortwave Infrared Airborne Spectrogrpahic Imager (SASI) hyperspectral data. Experimental results demonstrate that the proposed method significantly outperforms other classifiers, with an overall accuracy of 87.43% and a kappa value of 0.827 (5 labeled samples per class), which are respectively, 9%-30% and 0.1-0.3 higher than other counterparts.

An improved firefly algorithm (FA)-based band selection method is proposed for hyperspectral dimensionality reduction (DR). In this letter, DR is formulated as an optimization problem that searches a small number of bands from a hyperspectral data set, and a feature subset search algorithm using the FA is developed. To avoid employing an actual classifier within the band searching process to greatly reduce computational cost, criterion functions that can gauge class separability are preferred; specifically, the minimum estimated abundance covariance and Jeffreys-Matusita distances are employed. The proposed band selection technique is compared with an FA-based method that actually employs a classifier, the well-known sequential forward selection, and particle swarm optimization algorithms. Experimental results show that the proposed algorithm outperforms others, providing an effective option for DR.

Recently, collaborative representation for hyperspectral image analysis has received great interest. Due to the effectiveness of local manifold in a tangent space, this letter extends the collaborative representation classification (CRC) mechanism into the tangent space. Specifically, this letter uses simplified tangent distance and a new regularization term and designs a modified classifier innovatively. Moreover, two variants with weighted diagonal matrices to adaptively adjust the regularization terms are developed to further improve the classification performance. In the experiments, two real hyperspectral images were adopted for performance evaluation, and the experimental results demonstrate that the proposed algorithms can significantly improve classification results compared with the original CRC algorithm and other related classifiers.

It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives.

Sparse graph embedding (SGE) is a promising technique useful for the nonlinear feature extraction (FE) of hyperspectral images (HSIs). However, such images exhibit spatial variability and spectral multimodality, presenting challenges to existing FE methods, including SGE. To address this issue, this paper presents two novel SGE methods for HSI classification. One method, which is termed simultaneous SGE (SSGE), is designed to consider the spatial variability of spectral signatures by using a simultaneous sparse representation (SSR) model integrated with a shape-adaptive neighborhood building approach. In addition, a sparse graph is constructed via matrix computation based on sparse codes. Then, low-dimensional features are produced by employing linear graph embedding (LGE) based on the constructed sparse graph. The other method, which is termed simultaneous sparse multimanifold learning (SSMML), is proposed to handle the multimodality of an HSI. In SSMML, multiple views are generated to represent different modalities. Then, multiview-oriented submanifolds are produced by adopting SSGE, and they are further integrated via coregularization. SSGE is capable of modeling both local and global data structures. Furthermore, SSMML serves as a prototype that can model multimodal data structures. The proposed methods are evaluated by using sparse multinomial logistic regression for HSI classification. Experimental results with two popular hyperspectral data sets validate the good performance of the two methods in producing more representative low-dimensional features and yielding superior classification results compared with other related approaches.

Endmember extraction is an important task for hyperspectral analysis; the accurate identification of endmembers enables efficient spectral unmixing and classification. In the paper, a new endmember extraction algorithm based on a modified MSS approach with LP error as initial spectrum selection algorithm, and OPD measure as similarity is proposed. The endmembers extracted by modified MSS are more similar than that of MSS algorithm; from the experiments results, it has proved that our proposed method outperforms the existed MSS and N-FINDR algorithms.

Band clustering is applied to dimensionality reduction of hyperspectral imagery. Different from unsupervised clustering using all the pixels or supervised clustering requiring labeled pixels, the proposed semisupervised band clustering needs class spectral signatures only. After clustering, a cluster selection step is applied to select clusters to be used in the following data analysis. Initial conditions and distance metrics are also investigated to improve the clustering performance. The experimental results show that the proposed algorithm can outperform other existing methods with lower computational cost.

Band selection is often applied to reduce the dimensionality of hyperspectral imagery. When the desired object information is known, it can be achieved by finding the bands that contain the most object information. It is expected that these bands can provide an overall satisfactory detection and classification performance. In this letter, we propose a new supervised band-selection algorithm that uses the known class signatures only without examining the original bands or the need of class training samples. Thus, it can complete the task much faster than traditional methods that test bands or band combinations. The experimental result shows that our approach can generally yield better results than other popular supervised band-selection methods in the literature.

We propose a web-based collaborative education system (named BRIDGE) that promotes the use of high-quality, open-source code examples as educational materials to connect theories and practices of software engineering and computer science. BRIDGE promotes an innovative approach to answer the educational challenges in software engineering. It is comprised of three integrated subsystems: a code annotation system that provides an effective way to share the understanding of open- source code examples; a collaborative educational material creation system that helps build educational modules capable of connecting theories with real- world examples; and a collaborative course management system that allows educators to construct innovative courses based on the educational modules. BRIDGE encourages participation and collaborative knowledge sharing of software engineering professionals, educators, and students. BRIDGE is driven by this participation. The system will help students learn both technical knowledge and nontechnical skills highly desired in modern software engineering practices.

Recent demand for real time applications has given rise to a need for quality of service (QoS) in the Internet. Differentiated Services is one such effort currently pursued by IETF. Previous researchers found unfairness in the DiffServ network. To solve the unfairness problem, we propose a new TSW based three color marker (ItswTCM) which achieves proportional fair sharing of excess bandwidth among aggregates. We have compared the fairness of our proposed ItswTCM marker with srTCM, trTCM, and tswTCM. Results show that our proposed marker performs better than the other three schemes for low to middle network provision level (20%-70%); we believe this is the region where all well provisioned networks will operate. Results also show that our proposed marker is not as sensitive to the number of flows as previous markers. We point out that yellow packets play a significant role in achieving proportional fair sharing of excess bandwidth among aggregates. We conclude that in order to achieve proportional fair sharing of excess bandwidth, it is important to inject the right amount of yellow packets into the network.

The Internet was initially designed for non real-time data communications and hence does not provide any quality of service (QoS). The next generation Internet will be characterized by high speed and QoS guarantees. The aim of this paper is to develop a prioritized early packet discard (PEPD) scheme for ATM switches to provide service differentiation and QoS guarantee to end applications running over the next generation Internet. The proposed PEPD scheme differs from previous schemes by taking into account the priority of packets generated from different applications. We develop a Markov chain model for the proposed scheme and verify the model with simulation. Numerical results show that the results from the model and computer simulation are in close agreement. Our PEPD scheme provides service differentiation to end-to-end applications.