Image and Signal Processing Methods

Adaptive semisupervised feature selection without graph construction for very-high-resolution remote sensing images

[+] Author Affiliations
Xi Chen

Harbin Institute of Technology, School of Electronics and Information Engineering, 92 West Dazhi Street, Harbin 150001, China

Purdue University, Department of Forestry and Natural Resources, 610 Purdue Mall, West Lafayette 47907, United States

Jinzi Qi, Yushi Chen

Harbin Institute of Technology, School of Electronics and Information Engineering, 92 West Dazhi Street, Harbin 150001, China

Lizhong Hua

Xiamen University of Technology, School of Computer and Information Engineering, 600 Ligong Road, Xiamen 361024, China

Guofan Shao

Purdue University, Department of Forestry and Natural Resources, 610 Purdue Mall, West Lafayette 47907, United States

J. Appl. Remote Sens. 10(2), 025002 (Apr 12, 2016). doi:10.1117/1.JRS.10.025002
History: Received October 21, 2015; Accepted March 8, 2016
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Abstract.  Semisupervised feature selection methods can improve classification performance and enhance model comprehensibility with few labeled objects. However, most of the existing methods require graph construction beforehand, and the resulting heavy computational cost may bring about the failure to accurately capture the local geometry of data. To overcome the problem, adaptive semisupervised feature selection (ASFS) is proposed. In ASFS, the goodness of each feature is measured by linear objective functions based on loss functions and probability distribution matrices. By alternatively optimizing model parameters and automatically adjusting the probabilities of boundary objects, ASFS can measure the genuine characteristics of the data and then rank and select features. The experimental results attest to the effectiveness and practicality of the method in comparison with the latest and state-of-the-art methods on a Worldview II image and a Quickbird II image.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Xi Chen ; Jinzi Qi ; Yushi Chen ; Lizhong Hua and Guofan Shao
"Adaptive semisupervised feature selection without graph construction for very-high-resolution remote sensing images", J. Appl. Remote Sens. 10(2), 025002 (Apr 12, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.025002


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