Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery

Sun, Weiwei; Li, Chun; Li, Jialin; Lai, Yenming Mark; Li, Weiyue

doi:10.1117/1.jrs.8.083641

Cited by 163 publications

(87 citation statements)

References 39 publications

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“…First, we make comparison between RSLAD and other four state-of-the-art detectors, including GRX [15], LRX [15], CRD [32] and LRaSMD [12]. Second, we investigate the performance sensitivity of RSLAD to the number of randomly sampled columns (i.e., pixels) p. Third, we explore the performance sensitivity of RSLAD to the randomly projected dimension K. Finally, we analyze the performance sensitivity of RSLAD to the residual threshold ε.…”

Section: Resultsmentioning

confidence: 99%

“…The first dataset is the Pavia Center (PaviaC) dataset acquired by the reflective optics system imaging spectrometer (ROSIS) sensor [12,37]. It covers the Pavia Center in northern Italy and has accurate ground truth information.…”

Section: The Hsi Dataset Descriptionsmentioning

confidence: 99%

“…Figure 5b shows the ground objects of the anomalies and Figure 5c plots spectral curves of anomalies and main ground objects. The third dataset includes the Botswana data acquired from Remote Sensing Group of the University of Texas at Austin (www.csr.utexas.edu/hyperspectral/index.html) [12,48]. The dataset was collected by the EO-1 Hyperion sensor.…”

Section: The Hsi Dataset Descriptionsmentioning

confidence: 99%

“…Hyperspectral target detection can be applied in many realistic applications, including biophysical parameter retrieval [7], classification of complicated environments [8] and military target detection [9]. Compared with supervised target detection, unsupervised target detection, i.e., anomaly detection, does not require any prior knowledge of target spectral characteristic [10][11][12]. Anomaly detection methods have witnessed increasing interest due to their interesting applications [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…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. The pixels that have small L 2 norm values of the sparse coefficient vectors are selected as the anomalies.…”

Section: Introductionmentioning

confidence: 99%

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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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