.png){kind=icon}
返回
Spatial-Spectral Extraction for Hyperspectral Anomaly Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2021.3130908.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A novel hyperspectral anomaly detection method is proposed in this letter. This method is motivated by two important observations. First, there are hundreds of bands in a hyperspectral image (HSI), among which redundant bands and some noisy bands could be eliminated to increase the discriminability from the spectral domain. Meanwhile, considering the small amount of the anomalies, they may be neglected in the spatial clustering process, and the anomalies can be highlighted by eliminating the clustered result from the original HSI. In this way, the spatial-spectral extraction is proposed to detect the anomalies in the HSI. First, an iterative optimal neighborhood reconstruction (IONR) method is utilized to select the bands while preserving their divergence. Second, the spatial clustering is achieved by minimizing the intradistance in any given category. Finally, the spatial clustered HSI is eliminated from the band selected HSI and acts as the direct input for the detection process. Experimental results and data analysis have demonstrated the effectiveness of the proposed method.
Keyword:
Detectors
Anomaly detection
Tensors
Image reconstruction
Hyperspectral imaging
Spectral analysis
Sensors
Anomaly detection
hyperspectral image (HSI)
iterative band selection
spatial clustering
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
机构
暂无机构信息
引用论文
.png){kind=icon}
A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images
PREAI
分享收藏
查看更多内容