期刊
REMOTE SENSING
卷 10, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/rs10071033
关键词
hyperspectral imagery; target detection; preprocessing; tensor principal component analysis
类别
资金
- National Natural Science Foundation of China [61572133]
- Research fund for State Key Laboratory of Earth Surface Processes and Resource Ecology [2017-KF-19]
Traditional target detection (TD) algorithms for hyperspectral imagery (HSI) typically suffer from background interference. To alleviate this problem, we propose a novel preprocessing method based on tensor principal component analysis (TPCA) to separate the background and target apart. With the use of TPCA, HSI is decomposed into a principal component part and a residual part with the spatial-spectral information of the HSI being fully exploited, and TD is performed on the latter. Moreover, an effective distinction in scheme can be made between a HSI tensor's spatial and spectral domains, which is in line with the physical meanings. Experimental results from both synthetic and real hyperspectral data show that the proposed method outperforms other preprocessing methods in improving the TD accuracies. Further, target detectors that combine the TPCA preprocessing approach with traditional target detection methods can achieve better results than those of state-of-the-art methods aiming at background suppression.
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