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Remote Sensing Image Classification Algorithm Based on Texture Feature and Extreme Learning Machine

Xiangchun Liu1, Jing Yu2,Wei Song1, 3, *, Xinping Zhang1, Lizhi Zhao1, Antai Wang4

1 Media Computing Laboratory, School of Information Engineering, Minzu University of China, Beijing, 100081, China.
2 School of Telecommunication Engineering, Beijing Polytechnic, Beijing, 100176, China.
3 National Language Resource Monitoring and Research Center of Minority Languages, Minzu University of China, Beijing, 100081, China.
4 New Jersey Institute of Technology, Newark, NJ 07102, USA.

* Corresponding Author: Wei Song. Email: email.

Computers, Materials & Continua 2020, 65(2), 1385-1395.


With the development of satellite technology, the satellite imagery of the earth’s surface and the whole surface makes it possible to survey surface resources and master the dynamic changes of the earth with high efficiency and low consumption. As an important tool for satellite remote sensing image processing, remote sensing image classification has become a hot topic. According to the natural texture characteristics of remote sensing images, this paper combines different texture features with the Extreme Learning Machine, and proposes a new remote sensing image classification algorithm. The experimental tests are carried out through the standard test dataset SAT-4 and SAT-6. Our results show that the proposed method is a simpler and more efficient remote sensing image classification algorithm. It also achieves 99.434% recognition accuracy on SAT-4, which is 1.5% higher than the 97.95% accuracy achieved by DeepSat. At the same time, the recognition accuracy of SAT-6 reaches 99.5728%, which is 5.6% higher than DeepSat’s 93.9%.


Cite This Article

X. Liu, J. Yu, W. Song, X. Zhang, L. Zhao et al., "Remote sensing image classification algorithm based on texture feature and extreme learning machine," Computers, Materials & Continua, vol. 65, no.2, pp. 1385–1395, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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