Road Extraction from High Spatial Resolution Remote Sensing Image Based on Multi-Task Key Point Constraints

Abstract

To solve some problems of high spatial resolution remote sensing images caused by land coverage, building coverage and shading of trees, such as difficult road extraction and low precision, a road extraction method based on multi-Task key point constraints is put forward in this article based on Linknet. At the preprocessing stage, an auxiliary constraint task is designed to solve the connectivity problem caused by shading during road extraction from remote sensing images. At the encoding decoding stage, first, a position attention (PA) mechanism module and channel attention (CA) mechanism module are applied to realize the effective fusion of semantic information in the context during road extraction. Second, a multi-branch cascade dilated spatial pyramid (CDSP) is established with dilated convolution, by which the problem of loss of partial information during information extraction from remote sensing road image is solved and the detection accuracy is further improved. The method put forward in this article is verified through the experiment with public datasets and private datasets, revealing that the proposed method provides better performance than several state of the art techniques in terms of detection accuracy, recall, precision, and F1-score.