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Sensors
Volume 17
Issue 7
10.3390/s17071474
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Article

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

by
Xin Zhang
1,*,
Jintian Cui
1,2,
Weisheng Wang
3 and
Chao Lin
4
1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, 818 South Beijing Road, Urumqi 830011, China
4
Bureau of Upriver of Zhang Management, Hai River Management Committee Ministry of Water Resources of China, Handan 056006, China
*
Author to whom correspondence should be addressed.
Academic Editors: Changshan Wu and Shawn (Shixiong) Hu
Sensors 2017, 17(7), 1474; https://doi.org/10.3390/s17071474
Received: 10 March 2017 / Revised: 5 June 2017 / Accepted: 5 June 2017 / Published: 22 June 2017
(This article belongs to the Special Issue Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment)
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Abstract

To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. View Full-Text
Keywords: gray level co-occurrence matrix; direction measure; texture feature extraction; image classification gray level co-occurrence matrix; direction measure; texture feature extraction; image classification
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MDPI and ACS Style

Zhang, X.; Cui, J.; Wang, W.; Lin, C. A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm. Sensors 2017, 17, 1474. https://doi.org/10.3390/s17071474

AMA Style

Zhang X, Cui J, Wang W, Lin C. A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm. Sensors. 2017; 17(7):1474. https://doi.org/10.3390/s17071474

Chicago/Turabian Style

Zhang, Xin, Jintian Cui, Weisheng Wang, and Chao Lin. 2017. "A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm" Sensors 17, no. 7: 1474. https://doi.org/10.3390/s17071474

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