Research on the technology of terrain classification and change detection based on deep learning

Abstract

With the rapid development of urbanization in China, the change of land property is very rapid. The detection of land change is helpful to the government’s supervision and the development and protection of land resources. The traditional methods of land feature classification and change detection of remote sensing image generally adopt the methods of manual annotation and recognition. Due to the performance defects, they can not meet the requirements of remote sensing image business application. In this paper, the technology of satellite remote sensing image feature classification and change detection based on deep learning is proposed, and several demonstration cases of automatic classification and change detection system of buildings, roads, water bodies, forest land and other features based on high-resolution remote sensing image are formed. Relevant research results show that the technology can meet the requirements of remote sensing image classification and change detection, and the accuracy of the review is more than 90%.The method adopted can effectively solve the application needs of related fields.