Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240300095-8.doi: 10.11896/jsjkx.240300095

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Road Extraction from Complex Urban Remote Sensing Images Based on Multi-task Learning

WANG Kunyang1, LIU Yang1, YE Ning1, ZHANG Kai2   

  1. 1 School of Information Science and Technology,,Nanjing Forestry University,Nanjing 210037,China
    2 School of Instrument Science and Engineering,Southeast University,Nanjing 210018,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:WANG Kunyang,born in 1993,M.S.His main research interests include embedded technology and so on.
    YE Ning,born in 1967,doctor,professor.His main research interests include bioinformatics,data mining and machine learning.
  • Supported by:
    Future Network Research Fund Program (FNSRFP-2021-YB-17).

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Abstract: In this paper,we propose a new framework for road extraction from remote sensing images that aims to utilize the knowledge gained from road edge detection to improve the accuracy of road extraction.A multi-scale visual attention module that fuses multi-scale information and visual attention mechanisms is introduced in the study,and a cascading feature fusion module is constructed to integrate the network's prediction results at different scales.Based on this,we construct a multiscale visual attention network(MSVANet) containing encoders and decoders.A multi-task learning framework that incorporates the MSVANet is also proposed,and a particle swarm optimization algorithm(PSO) is used to optimize the automatic selection of the two learning rate hyperparameters of the multi-task learning framework.The training and testing results on the RNBD dataset show that the proposed method outperforms other road extraction methods in terms of various segmentation accuracy metrics and generalization ability.

Key words: Deep learning, Remote sensing image road extraction, Multi-task learning, Multi-scale visual attention network

CLC Number: 

  • TP391
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