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- W3101471640 abstract "Most traditional hyperspectral image (HSI) anomaly detection (AD) algorithms often consider the spectral difference between the anomaly and its background pixels, ignoring the local spatial structure information. An Adaptive Multi-layer Structure for hyperspectral image anomaly detection is proposed to handle this problem. This new algorithm not only calculates the spectral dimension difference between the pixel to be measured and the pixel in the background window, but also measures the spatial structure difference between the inner window and the background window. Based on this concept, a new hyperspectral image anomaly detection framework is constructed, which is composed of (1) anomaly detection layer, (2) background suppression layer, and (3) criteria layer. Experimental results show that compared with the traditional two-window models (i.e., global RX, local RX and KRX), this method makes full use of both the local spatial structure information and the spectral dimension information, adaptively inhibits the background, reduces the false alarm rate, and improves the detection effect of abnormal targets with fewer pixels." @default.
- W3101471640 created "2020-11-23" @default.
- W3101471640 creator A5019171727 @default.
- W3101471640 creator A5086729425 @default.
- W3101471640 date "2020-11-09" @default.
- W3101471640 modified "2023-09-23" @default.
- W3101471640 title "Adaptive Multi-Layer Structure for Anomaly Detection in Hyperspectral Images" @default.
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- W3101471640 doi "https://doi.org/10.1109/iciea48937.2020.9248272" @default.
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