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Journal of Sensors
Volume 2015 (2015), Article ID 142612, 10 pages
http://dx.doi.org/10.1155/2015/142612
Research Article

Bayesian Information Criterion Based Feature Filtering for the Fusion of Multiple Features in High-Spatial-Resolution Satellite Scene Classification

1Signal Processing Laboratory, School of Electronic Information, Wuhan University, Wuhan 430072, China
2Wireless Communication and Sensor Network Laboratory, School of Electronic Information, Wuhan University, Wuhan 430072, China

Received 12 November 2014; Accepted 18 February 2015

Academic Editor: Tianfu Wu

Copyright © 2015 Da Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a novel classification method for high-spatial-resolution satellite scene classification introducing Bayesian information criterion (BIC)-based feature filtering process to further eliminate opaque and redundant information between multiple features. Firstly, two diverse and complementary feature descriptors are extracted to characterize the satellite scene. Then, sparse canonical correlation analysis (SCCA) with penalty function is employed to fuse the extracted feature descriptors and remove the ambiguities and redundancies between them simultaneously. After that, a two-phase Bayesian information criterion (BIC)-based feature filtering process is designed to further filter out redundant information. In the first phase, we gradually impose a constraint via an iterative process to set a constraint on the loadings for averting sparse correlation descending below to a lower confidence limit of the approximated canonical correlation. In the second phase, Bayesian information criterion (BIC) is utilized to conduct the feature filtering which sets the smallest loading in absolute value to zero in each iteration for all features. Lastly, a support vector machine with pyramid match kernel is applied to obtain the final result. Experimental results on high-spatial-resolution satellite scenes demonstrate that the suggested approach achieves satisfactory performance in classification accuracy.