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Journal of Applied Remote Sensing

Adaptive semisupervised feature selection without graph construction for very-high-resolution remote sensing images
Author(s): Xi Chen; Jinzi Qi; Yushi Chen; Lizhong Hua; Guofan Shao
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Paper 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.

Paper Details

Date Published: 12 April 2016
PDF: 12 pages
J. Appl. Remote Sens. 10(2) 025002 doi: 10.1117/1.JRS.10.025002
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Xi Chen, Harbin Institute of Technology (China)
Purdue Univ. (United States)
Jinzi Qi, Harbin Institute of Technology (China)
Yushi Chen, Harbin Institute of Technology (China)
Lizhong Hua, Xiamen Univ. of Technology (China)
Guofan Shao, Purdue Univ. (United States)


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