SpectralSpatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest
文献类型：期刊论文
| 作者 | X. Song; S. Aryal; K. M. Ting; Z. Liu and B. He |
| 刊名 | IEEE Transactions on Geoscience and Remote Sensing
 |
| 出版日期 | 2022 |
| 卷号 | 60 |
| ISSN号 | 1962892 |
| DOI | 10.1109/TGRS.2021.3104998 |
| 英文摘要 | 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. |
| URL标识 | 查看原文 |
| 源URL | [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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