APLSSVM: Hybrid Entropy Models for Image Retrieval
International Journal of Intelligent Information Systems
Volume 4, Issue 2-2, March 2015, Pages: 9-14
Received: Nov. 8, 2014; Accepted: Nov. 12, 2014; Published: May 5, 2015
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Authors
Li Jun-yi, School of Electronic Information and Electrical engineering, Shanghai JiaoTong University, Shanghai, China
Li Jian-hua, School of Electronic Information and Electrical engineering, Shanghai JiaoTong University, Shanghai, China
Zhu Jin-hua, College of Network Communication Zhejiang Yuexiu University of Foreign Languages, Zhe Jiang, China
Chen Xiao-hui, Information Engineering School, Yulin University, Yulin, Shanxi, China
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Abstract
Aiming at properties of remote sensing image data such as high-dimension, nonlinearity and massive unlabeled samples, a kind of probability least squares support vector machine (PLSSVM) classification method based on hybrid entropy and L1 norm was proposed. Firstly, hybrid entropy was designed by combining quasi-entropy with entropy difference, which was used to select the most “valuable” samples to be labeled from massive unlabeled sample set. Secondly, a L1 norm distance measuring was used to further select and remove outliers and redundant data from the sample set to be labeled. Finally, based on originally labeled samples and screened samples, PLSSVM was gained through training. Experimental results on classification of ROSIS hyperspectral remote sensing images show that the overall accuracy and Kappa coefficient of the proposed classification method reach 89.90% and 0.8685 respectively. The proposed method can obtain higher classification accuracy with few training samples, which is much applicable to classification problem of remote sensing images.
Keywords
Remote Sensing Image, L1 Norm, Active Learning, PLSSVM (Probability Least Squares Support Vector Machine), Hybrid Entropy
To cite this article
Li Jun-yi, Li Jian-hua, Zhu Jin-hua, Chen Xiao-hui, APLSSVM: Hybrid Entropy Models for Image Retrieval, International Journal of Intelligent Information Systems. Special Issue: Content-based Image Retrieval and Machine Learning. Vol. 4, No. 2-2, 2015, pp. 9-14. doi: 10.11648/j.ijiis.s.2015040202.13
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