Hyperspectral Anomaly Detection: A survey

Su, Hongjun; Wu, Zhaoyue; Zhang, Huihui; Du, Qian

doi:10.1109/mgrs.2021.3105440

Cited by 174 publications

(64 citation statements)

References 128 publications

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“…and, the label of sample y is determined according to (2). Apart from the kernel method, Cai et al [70] proposed a collaborative representation classifier with probabilistic interpretation and called it a probabilistic collaborative representation classifier (ProCRC).…”

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confidence: 99%

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Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification

Lü

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recently, a series of collaborative representation (CR) methods have attracted much attention for hyperspectral images classification. In this paper, two CR-based dynamic ensemble selection (DES) methods using multi-view kernel collaborative subspace clustering (MVKCSC) and random subspace multi-view kernel collaborative subspace clustering (RSMVKCSC) are proposed. In order to combine spectral and spatial information to construct a region of competence (RoC), the multi-view learning strategy is used in the general DES method. Compared with traditional clustering methods, the MVC can more effectively utilize multi-feature information. Moreover, a new method of constructing the Laplacian matrix using kernel collaborative representation coefficients is proposed for clustering based on subspace clustering and CR theory. This method is called MVKCSC, which can obtain the clustering results by using kernel CR self-representation coefficients. In addition, to increase diversity of samples, the random subspace method (RSM) and MVKCSC are combined for RMVKCSC. Moreover, the algorithm can obtain better clustering results by constraining samples and features simultaneously. The effectiveness of the proposed methods is validated using three hyperspectral data sets with few samples. The experimental results show that both DES-MVKCSC and DES-RSMVKCSC outperform their single classifier counterparts. In particular, the proposed methods provide superior performance compared with the state-of-the-art DES methods.

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confidence: 99%

“…yperspectral image provides abundant spectral information in hundreds of contiguous spectral bands [1]. This property has led to hyperspectral images widely applied in many applications [2], such as environmental monitoring [3], agriculture [4], mineral exploration [5], military [6].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification

Lü

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…I N the recent years, anomaly detection has been extensively studied in the field of hyperspectral data analysis [1]. Its popularity is further enhanced by the ability to spot abnormal events or man-made targets in an unsupervised manner.…”

Section: Introductionmentioning

confidence: 99%

Low-Power Hyperspectral Anomaly Detector Implementation in Cost-Optimized FPGA Devices

Caba

Díaz²,

Barba

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Onboard data processing for on-the-fly decisionmaking applications has recently gained momentum in the field of remote sensing. In this context, hyperspectral anomaly detection has received a special attention since its main purpose lays on the identification of abnormal events in an unsupervised manner. Nevertheless, onboard real-time hyperspectral image processing still poses several challenges before becoming a reality. This is why there is an emerging trend towards the development of hardware-friendly algorithmic solutions embedded in reconfigurable devices. In this context, this work contributes with a hardware architecture that ensures a progressive line processing in time-sensitive applications limited by the scarcity of hardware resources. In this sense, we have implemented the state-of-theart HW-LbL-FAD detector on a reconfigurable hardware for a real-time performance. Specifically, we have selected a costoptimized FPGA (ZC7Z020-CLG484) to implement our solution whose results draw up a good trade-off between the following three features: time performance, energy consumption and cost. The experimental results indicate our hardware component is able to process hyperspectral images of 825x1024 pixels and 160 bands in 0.51 seconds with a power-budget of 1.3 watts and a device cost around 150 C. Regarding detection performance, the HW-LbL-FAD algorithm outperforms other state-of-the-art algorithms.

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“…Hyperspectral anomaly detection (HAD) [5] is an important branch of target detection. As opposed to matching target detection [6] with known information, HAD can automatically locate anomaly objects without any prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…The LRX adopts a slide dual-window model to find anomalies block by block. However, the multivariate Gaussian distribution is unable to model complex background, and the background modeling may be contaminated by anomalies [5]. There are two common strategies to improve the RX algorithm.…”

Section: Introductionmentioning

confidence: 99%

Locality Constrained Low Rank Representation and Automatic Dictionary Learning for Hyperspectral Anomaly Detection

Huang

Liu

2022

Remote Sensing

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Hyperspectral anomaly detection (HAD) as a special target detection can automatically locate anomaly objects whose spectral information are quite different from their surroundings, without any prior information about background and anomaly. In recent years, HAD methods based on the low rank representation (LRR) model have caught much attention, and achieved good results. However, LRR is a global structure model, which inevitably ignores the local geometrical information of hyperspectral image. Furthermore, most of these methods need to construct dictionaries with clustering algorithm in advance, and they are carried out stage by stage. In this paper, we introduce a locality constrained term inspired by manifold learning topreserve the local geometrical structure during the LRR process, and incorporate the dictionary learning into the optimization process of the LRR. Our proposed method is an one-stage algorithm, which can obtain the low rank representation coefficient matrix, the dictionary matrix, and the residual matrix referring to anomaly simultaneously. One simulated and three real hyperspectral images are used as test datasets. Three metrics, including the ROC curve, AUC value, and box plot, are used to evaluate the detection performance. The visualized results demonstrate convincingly that our method can not only detect anomalies accurately, but also suppress the background information and noises effectively. The three evaluation metrics also prove that our method is superior to other typical methods.

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Hyperspectral Anomaly Detection: A survey

Cited by 174 publications

References 128 publications

Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification

Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification

Low-Power Hyperspectral Anomaly Detector Implementation in Cost-Optimized FPGA Devices

Locality Constrained Low Rank Representation and Automatic Dictionary Learning for Hyperspectral Anomaly Detection

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