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Hyperspectral Anomaly Detection Based on Low-Rank Representation with Data-Driven Projection and Dictionary Construction
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral image anomaly detection is an increasingly important research topic in remote sensing images understanding and interpretation. Recently, low-rank representation-based methods have attracted extensive attention and achieved promising performances in hyperspectral anomaly detection. These methods assume that the hyperspectral data can be decomposed into two parts: the low-rank component representing the background and the residual part indicating the anomaly. In order to improve the
doi:10.1109/jstars.2020.2990457
fatcat:lpp4zovkyjgnzndf4lex4uc7ge