An road synthesis extraction method of remote sensing image based on improved DeepLabV3+ network

doi:10.13474/j.cnki.11-2246.2022.0171.

Abstract

Abstract: In the process of road extraction of complex scenes from remote sensing images, the shadows of trees and buildings as well as wasteland and open space are often interfered by many factors. In view of the problems of incomplete road information and loss of detail information in road extraction from DeepLabV3+ network model, this paper proposes a road extraction method of remote sensing image based on DeepLabV3 + network, which utilizes lightweight MobileNetV2 as the backbone network for feature extraction. The spatial pyramid pooling module is used to obtain multi-scale road information features to reduce the loss of details of road remote sensing images and improve the accuracy of road extraction. Experimental results of road extraction on the DeepGlobe dataset show that the proposed method can effectively reduce the computational complexity while ensuring that the accuracy is improved. In terms of pixel accuracy and intersection ratio, it reaches 79.7% and 64.3%, respectively, which are superior to DeepLabV3+ network and other classical network models, showing better road extraction ability.

Key words: road extraction, improved DeepLabV3+, MobileNetV2, space pyramid pool

CLC Number:

P237

REN Yuejuan, GE Xiaosan. An road synthesis extraction method of remote sensing image based on improved DeepLabV3+ network[J]. Bulletin of Surveying and Mapping, 2022, 0(6): 55-61.

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