Int. J. of Information and Communication Technology   »   2018 Vol.13, No.2

 

 

Title: Probability least squares support vector machine with L1 norm for remote sensing image retrieval

 

Author: Jinhua Zhu

 

Address: College of Network Communication, Zhejiang Yuexiu University of Foreign Languages, Shaoxing, China

 

Abstract: This paper proposes a probability least squares support vector machine (PLSSVM) classification method that remotely senses image data, like high-dimension, nonlinearity, and massive unlabelled samples. Hybrid entropy was designed by combining quasi-entropy with entropy difference, which was then used to select the most 'valuable' samples from a larger set to be labelled. An L1 norm distance measurement was then used to further select and remove outliers and redundant data. Finally, based on the originally labelled samples and the screened samples, the PLSSVM method was implemented through training, and it is also more efficient in than the tradition SVM in both accuracy and speed. The experimental results of the classification of ROSIS hyperspectral remote sensing images show that the overall accuracy and Kappa coefficient of the proposed classification method are more accurate than existing methods. The proposed method obtains a higher classification accuracy with fewer training samples, allowing it to be applicable to current problems of classification.

 

Keywords: remote sensing image; hybrid entropy; L1 norm; active learning; probability least squares support vector machine; PLSSVM.

 

DOI: 10.1504/IJICT.2018.090558

 

Int. J. of Information and Communication Technology, 2018 Vol.13, No.2, pp.208 - 218

 

Available online: 14 Mar 2018

 

 

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