Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 58, Issue 9, Pages 6664-6676Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.2978491
Keywords
Feature extraction; Hyperspectral imaging; Anomaly detection; Clustering algorithms; Forestry; Detectors; Hyperspectral anomaly detection (HAD); hyperspectral image (HSI); isolation forest; multiple features
Categories
Funding
- National Key Research and Development Program of China [2018YFB1403501]
- National Natural Science Foundation of China [61936014, 61772427, 61751202, U1803263]
- Key Area Research and Development Program of Shaanxi Province [2019ZDLGY17-07]
- Fundamental Research Funds for the Central Universities [G2019KY0501, 3102019PJ006]
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Hyperspectral anomaly detection (HAD) has drawn a significant attention of late due to its importance in many military and civilian applications. In this article, a fast hyperspectral anomaly detector that combines multiple features and isolation forest is proposed. This approach, which is based on the assumption that the anomalous pixels are more susceptible to isolation than the background pixels, consists of two main parts. First, the spectral, Gabor, extended morphological profile (EMP) and extended multiattribute profile (EMAP) features are extracted from the hyperspectral image (HSI). Next, the isolation forest of each feature is constructed using the subsampling strategy. This combination of multiple features can exploit both the spectral and spatial information of the HSI, thereby improving the anomaly detection performance significantly. Compared with eight state-of-the-art HAD methods, the experimental results on four real hyperspectral data sets demonstrate that the performance of our proposed approach is quite competitive in terms of detection accuracy and running time.
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