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

Xungen Li,Shuaishuai Lv,Haibin Yu,Zhan Zhang,Mian Pan,Qi Ma

doi:10.1109/access.2021.3094536

Xungen Li, Shuaishuai Lv + Show 4 more

Open Access

https://doi.org/10.1109/access.2021.3094536

Copy DOI

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.

Highlights

In modern society, roads are an important identification object in maps and geographic information systems
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 multitask key point constraints is put forward in this article based on Linknet
DLinkNet improves the accuracy of road extraction by adding a dilated convolution structure to LinkNet to increase the receptive field of the feature map, and the IoU, F1 score, Precision and Recall are 67.63%, 80.34%, 75.28% and 86.13% on RSR Dataset

Summary

Introduction

Roads are an important identification object in maps and geographic information systems. How to extract road information from remote sensing images quickly and accurately has aroused the attention of many scholars [5,6,7,8]. During road extraction from remote sensing images, the preprocessing of data is usually carried out first to extract road features, and roads are classified based on pixel points to get the final result. According to different research methods of road extraction, road extraction methods can be classified into the traditional method and the deep learning method. Roads are usually recognized and extracted by establishing a feature model based on the basic feature of roads and human experience. Chaudhuri D[13] et al extracted roads through enhancing morphological features of directions and deduced road sections accurately based on the feature of road sections. Unsalan[15] et al extracted the initial edge feature of roads and extracted roads

Methods

Results

Conclusion