期刊
REMOTE SENSING
卷 13, 期 20, 页码 -出版社
MDPI
DOI: 10.3390/rs13204102
关键词
anomaly detection; spectral unmixing; structured sparse representation; archetypal analysis unmixing
类别
资金
- National Natural Science Foundation of China [61701123]
- Guangdong Provincial Key Laboratory of Cyber-Physical System [2020B1212060069]
- High Resolution Earth Observation Major Project [83-Y40G33-9001-18/20]
The proposed method combines spectral unmixing and structured sparse representation to enhance anomaly target detection accuracy. By utilizing background features at a sub-pixel level, the method significantly improves the performance of anomaly target detection.
Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing and structure sparse representation to accurately extract the pure background features and to establish a structured sparse representation model at a sub-pixel level by using the Archetypal Analysis (AA) scheme. Specifically, spectral unmixing with AA is used to unmix the spectral data to obtain representative background endmember signatures. Moreover the unmixing reconstruction error is utilized for the identification of the target. Structured sparse representation is also adopted for anomaly target detection by using the background endmember features from AA unmixing. Moreover, both the AA unmixing reconstruction error and the structured sparse representation reconstruction error are integrated together to enhance the anomaly target detection performance. The proposed method exploits background features at a sub-pixel level to improve the accuracy of anomaly target detection. Comparative experiments and analysis on public hyperspectral datasets show that the proposed algorithm potentially surpasses all the counterpart methods in anomaly target detection.
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