@Article{cmc.2020.011308,
AUTHOR = {Xiangchun Liu, Jing Yu,Wei Song, Xinping Zhang, Lizhi Zhao, Antai Wang},
TITLE = {Remote Sensing Image Classification Algorithm Based on Texture  Feature and Extreme Learning Machine},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1385--1395},
URL = {http://www.techscience.com/cmc/v65n2/39882},
ISSN = {1546-2226},
ABSTRACT = {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%.},
DOI = {10.32604/cmc.2020.011308}
}