Hyperspectral band selection based on triangular factorization

Wenqiang Zhang; Xiaorun Li; Liaoying Zhao

doi:10.1117/1.JRS.11.025007

4 May 2017 Hyperspectral band selection based on triangular factorization

Wenqiang Zhang, Xiaorun Li, Liaoying Zhao

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), National Nature Science Foundation of China, Natural Science Foundation of China, Shanghai Aerospace Science and Technology Innovation Fund, Joint Fund Project of Chinese Ministry of Education, Joint Funds of the Ministry of Education of China

Journal of Applied Remote Sensing, Vol. 11, Issue 2, 025007 (May 2017). https://doi.org/10.1117/1.JRS.11.025007

Abstract

We proposed an efficient unsupervised band selection method, maximum ellipsoid volume triangular factorization (MEV–TF), which is based on MEV and TF. MEV band selection regards the bands with the maximum determinant of the covariance matrix as the optimal band collection. By adopting TF, MEV–TF replaces the matrix determinant with scalar multiplication and achieves incremental calculation, which decreases the computational cost significantly. MEV–TF tries to select bands with large information and low correlation. Experimental results on different real data verify the efficiency of the proposed method.

Citation Download Citation

Wenqiang Zhang, Xiaorun Li, and Liaoying Zhao "Hyperspectral band selection based on triangular factorization," Journal of Applied Remote Sensing 11(2), 025007 (4 May 2017). https://doi.org/10.1117/1.JRS.11.025007

Received: 16 January 2017; Accepted: 13 April 2017; Published: 4 May 2017

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