Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 13, Issue 8, Pages 1129-1133Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2016.2572090
Keywords
Hyperspectral target detection; least squares; low-rank regularization; matched subspace detector (MSD)
Categories
Funding
- National Natural Science Foundation of China [61471199, 61301215, 61301217, 61502206, 11431015]
- Fundamental Research Funds for the Central Universities [30915012204]
- Research Funds of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks [WSNLBKF201507]
- Nature Science Foundation of Jiangsu Province [BK20150523]
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Target detection plays an important role in the field of hyperspectral image (HSI) remote sensing. In this letter, a novel matched subspace detector based on low-rank regularized least squares (LRLS-MSD) is proposed for hyperspectral target detection. As pixels in an HSI have global correlation and can be represented in subspace, the low-rank regularization is introduced in the least squares model. An effective algorithm is presented to solve the problem. Then, the detection results are generated according to the generalized likelihood ratio test with statistical hypotheses. The experimental results suggest an advantage of the low-rank regularization over other classical target detection methods.
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