TY - JOUR
T1 - Remote sensing image classification algorithm based on texture feature and extreme learning machine
AU - Liu, Xiangchun
AU - Yu, Jing
AU - Song, Wei
AU - Zhang, Xinping
AU - Zhao, Lizhi
AU - Wang, Antai
N1 - Funding Information:
Funding Statement: This work was supported in part by national science foundation project of P.R.China under Grant No.61701554 and State Language Commission Key Project (ZDl135-39), First class courses (Digital Image Processing: KC2066), MUC 111 Project, Ministry of Education Collaborative Education Project (201901056009; 201901160059; 201901238038).
Publisher Copyright:
© 2020 Tech Science Press. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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%.
AB - 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%.
KW - Extreme learning machine
KW - Gray level co-occurrence matrix
KW - Image classification
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U2 - 10.32604/cmc.2020.011308
DO - 10.32604/cmc.2020.011308
M3 - Article
AN - SCOPUS:85091159641
SN - 1546-2218
VL - 65
SP - 1385
EP - 1395
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 2
ER -