Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition

Cui, Xiaoguang; Tian, Yuan; Weng, Lubin; Yang, Yiping

doi:10.1117/12.2050229

Cited by 17 publications

(10 citation statements)

References 7 publications

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“…Thus, matrix A can be assumed to have a sparse property. Therefore, the HSI dataset H can be decomposed into Equation (2) by using the LRaSMD technique [22][23][24][25][26]. Figure 1 shows the decomposed data cubes of different parts.…”

Section: Lrasmd Model For Hsi Datasetmentioning

confidence: 99%

Low-Rank and Sparse Matrix Decomposition with Cluster Weighting for Hyperspectral Anomaly Detection

2018

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Hyperspectral anomaly detection plays an important role in the field of remote sensing. It provides a way to distinguish interested targets from the background without any prior knowledge. The majority of pixels in the hyperspectral dataset belong to the background, and they can be well represented by several endmembers, so the background has a low-rank property. Anomalous targets usually account for a tiny part of the dataset, and they are considered to have a sparse property. Recently, the low-rank and sparse matrix decomposition (LRaSMD) technique has drawn great attention as a method for solving anomaly detection problems. In this letter, a new anomaly detection method based on LRaSMD and cluster weighting is proposed. We concentrate on the sparse part, which contains most of anomaly information, and calculate the initial anomaly matrix based on this part. To suppress background regions and discriminate anomalies from the background more distinctly, a weighting strategy in terms of the clustering result is used, and then the anomaly matrix is updated. The judgement of anomalies is made according to the responses on the matrix. Our proposed method considers the characteristics of anomalies from the spectral dimension and the spatial distribution simultaneously. Experiments on three hyperspectral datasets demonstrate the outstanding performance of the proposed method.

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Section: Lrasmd Model For Hsi Datasetmentioning

confidence: 99%

Low-Rank and Sparse Matrix Decomposition with Cluster Weighting for Hyperspectral Anomaly Detection

2018

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“…Therefore, the LRaSMD method decomposes the original HSI into low-rank, sparse, and noise matrices. Some LRaSMD methods show relatively good performance in hyperspectral anomaly detection, including Euclidean distance-based LRaSMD (EDL-RaSMD) [34], two-norm-based LRaSMD [35], the LRaSMD-based Mahalanobis distance method for hyperspectral anomaly detection (LSMAD) [36] and the parts representationbased LRaSMD (PRLRaSAD) [4].…”

Section: Introductionmentioning

confidence: 99%

A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy Bilateral Smoothing and Extended Multi-Attribute Profile

Liu

Zhang

et al. 2021

Remote Sensing

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To address the difficulty of separating background materials from similar materials associated with the use of “single-spectral information” for hyperspectral anomaly detection, a fast hyperspectral anomaly detection algorithm based on what we term the “greedy bilateral smoothing and extended multi-attribute profile” (GBSAED) method is proposed to improve detection precision and operation efficiency. This method utilizes “greedy bilateral smoothing” to decompose the low-rank part of a hyperspectral image (HSI) dataset and calculate spectral anomalies. This process improves the operational efficiency. Then, the extended multi-attribute profile is used to extract spatial anomalies and restrict the shape of anomalies. Finally, the two components are combined to limit false alarms and obtain appropriate detection results. This new method considers both spectral and spatial information with an improved structure that ensures operational efficiency. Using five real HSI datasets, this study demonstrates that the GBSAED method is more robust than eight representative algorithms under diverse application scenarios and greatly improves detection precision and operational efficiency.

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“…The sparsity score estimation framework (SSEF) detector counts the frequency of each dictionary atom for hyperspectral data construction in sparse representation, and it estimates the anomalies using the sparsity score matrix of all pixels [35]. The low-rank and sparse decomposition (LSD) formulates the detection of anomalies as a RPCA problem in the local image region and finds the anomalies by soring each pixel by the norm of its corresponding sparse coefficient vector [36]. The low-rank and sparse matrix decomposition based anomaly detection (LRaSMD) [12] improved the RPCA model by separating the noise term from the anomaly term in the sparse noise matrix.…”

Section: Introductionmentioning

confidence: 99%

A Randomized Subspace Learning Based Anomaly Detector for Hyperspectral Imagery

Sun

Tian

et al. 2018

Remote Sensing

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This paper proposes a randomized subspace learning based anomaly detector (RSLAD) for hyperspectral imagery (HSI). Improved from robust principal component analysis, the RSLAD assumes that the background matrix is low-rank, and the anomaly matrix is sparse with a small portion of nonzero columns (i.e., column-wise). It also assumes the anomalies do not lie in the column subspace of the background and aims to find a randomized subspace of the background to detect the anomalies. First, random techniques including random sampling and random Hadamard projections are implemented to construct a coarse randomized columns subspace of the background with reduced computational cost. Second, anomaly columns are searched and removed from the coarse randomized column subspace by solving a series of least squares problems, resulting in a purified randomized column subspace. Third, the nonzero columns in the anomaly matrix are located by projecting all the pixels on the orthogonal subspace of the purified subspace, and the anomalies are finally detected based on the L 2 norm of the columns in the anomaly matrix. The detection performance of RSLAD is compared with four state-of-the-art methods, including global Reed-Xiaoli (GRX), local RX (LRX), collaborative-representation based detector (CRD), and low-rank and sparse matrix decomposition base anomaly detector (LRaSMD). Experimental results show good detection performance of RSLAD with lower computational cost. Therefore, the proposed RSLAD offers an alternative option for hyperspectral anomaly detection.

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Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition

Cited by 17 publications

References 7 publications

Low-Rank and Sparse Matrix Decomposition with Cluster Weighting for Hyperspectral Anomaly Detection

Low-Rank and Sparse Matrix Decomposition with Cluster Weighting for Hyperspectral Anomaly Detection

A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy Bilateral Smoothing and Extended Multi-Attribute Profile

A Randomized Subspace Learning Based Anomaly Detector for Hyperspectral Imagery

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