Multiple Features and Isolation Forest-Based Fast Anomaly Detector for Hyperspectral Imagery
- 18 March 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 58 (9), 6664-6676
- https://doi.org/10.1109/tgrs.2020.2978491
Abstract
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.Keywords
Funding Information
- National Basic Research 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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