Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery
作者： Weiwei Sun , Chun Liu , Jialin Li , Yenming Mark Lai , Weiyue Li
关键词: Euclidean distance 、 Pattern recognition 、 Sparse matrix 、 Detector 、 Mathematics 、 Hyperspectral imaging 、 Anomaly (natural sciences) 、 Pixel 、 Thresholding 、 Artificial intelligence 、 Anomaly detection
摘要: A low-rank and sparse matrix decomposition (LRaSMD) detector has been proposed to detect anomalies in hyperspectral imagery (HSI). The detector assumes background images are low-rank while anomalies are gross errors that are sparsely distributed throughout the image scene. By solving a constrained convex optimization problem, the LRaSMD detector separates the anomalies from the background. This protects the background model from corruption. An anomaly value for each pixel is calculated using the Euclidean distance, and …
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