Precise Extraction of Built-Up Area Using Deep Features
Yihua Tan, Shengzhou Xiong, Yaming Li · 2018
Built-up area is one of the most important objects in remote sensing image analysis, therefore extracting built-up area automatically has attracted wide attention. Deep convolution neural network (CNN) was proposed to improve poor generalization ability of artificial features which had been adopted by traditional automatic extraction methods. In this paper, a more efficient CNN model is proposed to extract the deep features of remote sensing images, and then a graph model based on deep features is constructed to the full image for built-up area extraction. The experiments demonstrate that it has very good performance on the satellite remote sensing image data set.