SpectralSpatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

doi:10.1109/TGRS.2021.3104998

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	SpectralSpatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest
	X. Song; S. Aryal; K. M. Ting; Z. Liu and B. He
	2022
发表期刊	IEEE Transactions on Geoscience and Remote Sensing
ISSN	1962892
卷号	60
摘要	Anomaly detection in hyperspectral image (HSI) is affected by redundant bands and the limited utilization capacity of spectralspatial 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 spectralspatial 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. 2021 IEEE.
DOI	10.1109/TGRS.2021.3104998
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收录类别	ei
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文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/67104
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	X. Song,S. Aryal,K. M. Ting,et al. SpectralSpatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60.
APA	X. Song,S. Aryal,K. M. Ting,&Z. Liu and B. He.(2022).SpectralSpatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest.IEEE Transactions on Geoscience and Remote Sensing,60.
MLA	X. Song,et al."SpectralSpatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest".IEEE Transactions on Geoscience and Remote Sensing 60(2022).

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