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20 February 2019 Sparse and low-rank matrix decomposition-based method for hyperspectral anomaly detection
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Abstract
A sparse and low-rank matrix decomposition-based method is proposed for anomaly detection in hyperspectral data. High-dimensional data are decomposed into low-rank and sparse matrices representing background and anomalies, respectively. The problem of the decomposition process is defined from the dictionary learning point of view. Therefore, our way of obtaining these matrices differs from previous studies. It aims to find a correct partition of the data and separate anomaly pixels from the background. After decomposition, Mahalanobis distance is applied to the sparse part of the data to get anomaly locations. Three hyperspectral data sets are used for evaluation. Experimental results suggest that anomaly detection performance of the proposed method surpasses those of the state-of-the-art methods.
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$25.00 © 2019 SPIE
Fatma Küçük, Behcet U. Töreyin, and Fatih Vehbi Çelebi "Sparse and low-rank matrix decomposition-based method for hyperspectral anomaly detection," Journal of Applied Remote Sensing 13(1), 014519 (20 February 2019). https://doi.org/10.1117/1.JRS.13.014519
Received: 26 April 2018; Accepted: 23 January 2019; Published: 20 February 2019
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