Affiliations: [a] School of Information Engineering, Xijing University, Xi’an, Shaanxi, China
| [b] Engineering Training Center, Xi’an University of Technology, Xi’an, Shaanxi, China
Correspondence:
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Corresponding author. Changqing Yu, School of Information Engineering, Xijing University, Xi’an, Shaanxi, China. E-mail: yuchangqing@xijing.edu.cn.
Abstract: With the development of modern remote sensing technology, remote sensing images have become one of the powerful tools for people to understand the Earth and its surroundings. However, there is currently no good classification algorithm that can accurately classify images. In order to accurately classify remote sensing images, this paper studies the content of the article by using fuzzy C-means clustering algorithm and radial basis neural network (RBF). The classification accuracy of SIRI-WHU dataset was analyzed by using the classification accuracy evaluation index such as overall accuracy and Kappa coefficient. The Kappa coefficient of vegetation classification in SIRI-WHU dataset was 0.9678, and the overall accuracy reached 97.18%. According to the classification problem of remote sensing image, according to the characteristics of remote sensing image, the improved model Alex Net-10-FCM is used to classify the remote sensing image dataset, and very high classification accuracy is obtained.
