期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 60, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3104998
关键词
Detectors; Hyperspectral imaging; Anomaly detection; Forestry; Feature extraction; Vegetation; Tensors; Anomaly detection; hyperspectral image (HSI); isolation forest (iForest); spectral-spatial information
类别
资金
- National Natural Science Foundation of China (NSFC) [61801455]
- National Natural Science Foundation of Jilin Province [YDZJ202101ZYTS048]
An improved Isolation Forest algorithm was proposed for anomaly detection in hyperspectral images, utilizing spatial and spectral information to detect anomaly pixels. Experimental results showed that the proposed detector outperformed other state-of-the-art methods.
Anomaly detection in hyperspectral image (HSI) is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Furthermore, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the relative mass isolation forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the entropy rate segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral datasets demonstrate that the proposed detector outperforms other state-of-the-art methods.
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