Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance

Remote Sensing

卷:12

Shenglin Li¹  Peizhang Fang¹  Dehui Xiong¹  Chu He¹  Mingsheng Liao² 

[1] Electronic Information School, Wuhan University, Wuhan 430072, China;

[2] State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;

关键词: remote sensing image;    semantic segmentation;    edge information;    Edge-FCN;   

DOI  :  10.3390/rs12091501

来源: DOAJ

【 摘 要 】

Semantic segmentation is an important field for automatic processing of remote sensing image data. Existing algorithms based on Convolution Neural Network (CNN) have made rapid progress, especially the Fully Convolution Network (FCN). However, problems still exist when directly inputting remote sensing images to FCN because the segmentation result of FCN is not fine enough, and it lacks guidance for prior knowledge. To obtain more accurate segmentation results, this paper introduces edge information as prior knowledge into FCN to revise the segmentation results. Specifically, the Edge-FCN network is proposed in this paper, which uses the edge information detected by Holistically Nested Edge Detection (HED) network to correct the FCN segmentation results. The experiment results on ESAR dataset and GID dataset demonstrate the validity of Edge-FCN.

【 授权许可】

Unknown