Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 58, Issue 1, Pages 319-329Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2936308
Keywords
Anomaly detection; hyperspectral image (HSI); Isolation Forest (iForest); kernel method
Categories
Funding
- National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]
- National Natural Science Foundation of China [61871179, 61601179]
- Science and Technology Plan Project Fund of Hunan Province [CX2018B171, 2017RS3024, 2018TP1013]
- Science and Technology Talents Program of the Hunan Association for Science and Technology [2017TJ-Q09]
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In this article, a novel hyperspectral anomaly detection method with kernel Isolation Forest (iForest) is proposed. The method is based on an assumption that anomalies rather than background can be more susceptible to isolation in the kernel space. Based on this idea, the proposed method detects anomalies as follows. First, the hyperspectral data are mapped into the kernel space, and the first $K$ principal components are used. Then, the isolation samples in the image are detected with the iForest constructed using randomly selected samples in the principal components. Finally, the initial anomaly detection map is iteratively refined with locally constructed iForest in connected regions with large areas. Experimental results on several real hyperspectral data sets demonstrate that the proposed method outperforms other state-of-the-art methods.
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