期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 55, 期 10, 页码 5600-5611出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2017.2710145
关键词
Anomaly detection; attribute filtering; Boolean map; edge-preserving filtering; hyperspectral image
类别
资金
- National Natural Science Foundation of China [61601179]
- National Natural Science Fund of China for Distinguished Young Scholars [61325007]
- National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]
- Science and Technology Plan Projects Fund of Hunan Province [2015WK3001]
A novel method for anomaly detection in hyperspectral images is proposed. The method is based on two ideas. First, compared with the surrounding background, objects with anomalies usually appear with small areas and distinct spectral signatures. Second, for both the background and the objects with anomalies, pixels in the same class are usually highly correlated in the spatial domain. In this paper, the pixels with specific area property and distinct spectral signatures are first detected with attribute filtering and a Boolean map-based fusion approach in order to obtain an initial pixel-wise detection result. Then, the initial detection result is refined with edge-preserving filtering to make full use of the spatial correlations among adjacent pixels. Compared with other widely used anomaly detection methods, the experimental results obtained on real hyperspectral data sets including airport, beach, and urban scenes demonstrate that the performance of the proposed method is quite competitive in terms of computing time and detection accuracy.
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