In this paper, we propose a novel sparsity-based algorithm for anomaly detection in hyperspectral imagery. The algorithm is based on the concept that a background pixel can be approximately represented as a sparse linear combination of its spatial neighbors while an anomaly pixel cannot if the anomalies are removed from its neighborhood. To be physically meaningful, the sum-to-one and nonnegativity constraints are imposed to abundance vector based on the linear mixture model, and the upper bound constraint on sparsity level is removed for better recovery of the test pixel. First, the proposed method utilizes the redundant background information to automatically remove anomalies from the background dictionary. Then, the reconstruction error obtained by the new background dictionary is directly used for anomaly detection. Moreover, a kernel version of the proposed method is also derived to completely exploit the nonlinear feature of hyperspectral data. An important advantage of the proposed methods is their capability to adaptively model the background even when some anomaly pixels are involved. Extensive experiments have been conducted on three real hyperspectral data sets. It is demonstrated that the proposed detectors achieve a promising detection performance with a relatively low computational cost.

In this paper, we introduce a new approach to address the challenge of generalization in hyperspectral anomaly detection (AD). Our method eliminates the need for adjusting parameters or retraining on new test scenes as required by most existing methods. Employing an image-level training paradigm, we achieve a general anomaly enhancement network for hyperspectral AD that only needs to be trained once. Trained on a set of anomaly-free hyperspectral images with random masks, our network can learn the spatial context characteristics between anomalies and background in an unsupervised way. Additionally, a plug-and-play model selection module is proposed to search for a spatial-spectral transform domain that is more suitable for AD task than the original data. To establish a unified benchmark to comprehensive evaluate our method and existing methods, we develop a large-scale hyperspectral AD dataset (HAD100) that includes 100 real test scenes with diverse anomaly targets. In comparison experiments, we combine our network with a parameter-free detector, and achieve the optimal balance between detection accuracy and inference speed among state-of-the-art AD methods. Experimental results also show that our method still achieves competitive performance when the training and test set are captured by different sensor devices. Our code is available at https://github.com/ZhaoxuLi123/AETNet.

Deep subspace clustering network has shown its effectiveness in hyperspectral image (HSI) clustering. However, there are two major challenges that need to be addressed: 1) lack of effective supervision for feature learning; and 2) negative effect caused by the high redundancy of the global dictionary atoms. In this article, we propose an end-to-end trainable network for HSI clustering. Specifically, to ensure the extracted features are well-suited to subsequent subspace clustering, the cluster assignments with high confidence are employed as pseudo-labels to supervise the feature learning process. Then, an adaptive self-expressive coefficient matrix initialization strategy is designed to reduce the dictionary redundancy, where the spectral similarity between each target sample and its neighbors is modeled via the k-nearest neighbor graph to guide the initialization. Experimental results on three public HSI datasets demonstrate the effectiveness of the proposed method. In particular, our method outperforms several state-of-the-art HSI clustering methods, and achieves overall accuracy of 100% on both SalinasA and Pavia University datasets.

Hyperspectral anomaly detection (HAD) is regarded as an indispensable, pivotal technology in remote sensing and earth science domains. Nevertheless, most existing detection approaches for anomaly targets flatten 3-D hyperspectral images (HSIs) with spatial and spectral information into 2-D spectral vector data, which virtually breaks up the internal spatial structure in HSIs and degenerates the detection performance. To this end, we directly consider the HSI data cube as a 3-D tensor and develop a novel tensor low-rank approximation (TLRA) detection algorithm to separate the sparse anomalous component from the background with low-rank characteristics. Then, in light of the multi-subspace structure in heterogeneous backgrounds, we utilize multiple subspace learning (MSL) theory to encode the background tensor with a coefficient tensor and corresponding dictionary tensor. In addition, considering that different singular values indicate different information quantities and should be penalized to different extents, we introduce a tighter tensor rank surrogate named the ϵ-shrinkage tensor nuclear norm (ϵ-TNN) to recover the low-rank component more accurately. Meanwhile, concerning the sparse anomaly target, the l _2,1 constraint is incorporated to represent the group sparsity of the abnormal component. Finally, an effective iterative optimization algorithm based on the alternating direction method of multipliers (ADMM) is devised to solve the proposed TLRA-MSL model. We conduct extensive experiments on six hyperspectral datasets to prove the effectiveness and robustness of our method. The experimental results illustrate that better detection performance is obtained using the proposed model compared with other state-of-the-art algorithms.

Hyperspectral anomaly detection (HAD) aims to distinguish anomalies from background-by-background modeling. Deep learning has been applied to HAD and achieves promising detection results. However, there exist several issues that need to be addressed: 1) unrealistic Gaussian assumption on the latent representations may limit its application; 2) deep features are not well-suited to anomaly detection due to the separation between feature learning and anomaly detection; 3) lack of adequate exploitation of spectral-spatial features; 4) negative effect caused by spectral band redundancy. In this article, we propose an end-to-end trainable deep one-class classification network for HAD. Specifically, a minimal enclosing hypersphere is trained to involve the deep features of background samples. These background samples are selected by a density clustering-based method. In this way, feature learning and anomaly detection are incorporated into a unified framework. Meanwhile, there is no explicit Gaussian assumption on the background features. Moreover, due to the complementarity of spectral and spatial features, a novel feature fusion strategy is proposed to fuse spectral and spatial features extracted by a two-stream deep convolutional autoencoder network. Finally, a band attention module is used to automatically learn small weights for redundant bands and thus reduce the negative effect caused by redundant bands. Experimental results on five public datasets demonstrate the superiority of the proposed method compared to several state-of-the-art HAD methods in the detection performance.

Hyperspectral anomaly detection (HAD) aims at distinguishing anomalies from background in an unsupervised manner. Auto-encoder and its variant-based methods have achieved promising detection performance in HAD. However, most existing methods neglect to exploit the local structure information of hyperspectral images that reflects the underlying relationships between each pixel and its surroundings. Hence, the representation capabilities of the networks are restricted. Moreover, reconstruction of anomalies during training compels the networks to learn abnormal patterns, and thus reduces the spectral differences between background and anomalies. To address these problems, a spectral difference guided graph attention auto-encoder network is proposed for HAD. Specifically, the relationships among samples are modeled by a graph attention encoder, where a spectral sharpening constraint is introduced to guide the attention coefficient learning. In this way, the encoder can also represent the spectral differences between central nodes and their neighbors. Then, a learnable graph attention decoder is constructed to reconstruct node attributes and obtain the node reconstruction errors for HAD. Besides, a background purification method is proposed to generate superpixel-level samples to suppress anomaly reconstruction during training. Extensive quantitative and qualitative evaluations on six real datasets and one synthetic dataset show that the proposed method achieves competitive detection performance as compared to the state-of-the-art HAD methods.