Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 382-385.doi: 10.11896/jsjkx.201100184

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Feature Classification Method Based on Improved DeeplabV3+

ZHU Rong, YE Kuan, YANG Bo, XIE Huan, ZHAO Lei   

  1. Beijing Institute of Electrical Technology of State Grid,Beijing 100000,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:ZHU Rong,master,engineer.His main research interests include power safety management and so on.

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Abstract: The original DeeplabV3+ algorithm is not accurate enough for the edge segmentation of UAV aerial images,and the road segmentation is discontinuous.Therefore,in order to solve these problems,this paper improves the DeeplabV3+ algorithm.Firstly,the feature fusion is carried out in the coding stage to enhance the semantic information of the shallow feature map.Secondly,the boundary extraction branch module is added to the segmentation network structure,and Canny edge detection algorithm is used to extract the real boundary information for supervision training,so that the network can segment the edge of ground objects.Finally,in the decoding stage of the network,more shallow features are fused.The experimental results show that the mIoU value of the proposed method is 80.92%,which is 6.35% higher than that of the DeeplabV3+ algorithm,and can effectively classify the ground objects.

Key words: DeeplabV3+, Edge detection, Land cover classification, Remote sensing image, Semantic segmentation

CLC Number: 

  • TP274
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