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FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images
Cheng, Dongcai; Meng, Gaofeng; Xiang, Shiming; Pan, Chunhong
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
2017-12-01
Volume10Issue:12Pages:5769-5783
SubtypeArticle
AbstractSea-land segmentation and ship detection are two prevalent research domains for optical remote sensing harbor images and can find many applications in harbor supervision and management. As the spatial resolution of imaging technology improves, traditional methods struggle to perform well due to the complicated appearance and background distributions. In this paper, we unify the above two tasks into a single framework and apply the deep convolutional neural networks to predict pixelwise label for an input. Specifically, an edge aware convolutional network is proposed to parse a remote sensing harbor image into three typical objects, e. g., sea, land, and ship. Two innovations are made on top of the deep structure. First, we design a multitask model by simultaneously training the segmentation and edge detection networks. Hierarchical semantic features fromthe segmentation network are extracted to learn the edge network. Second, the outputs of edge pipeline are further employed to refine entire model by adding an edge aware regularization, which helps our method to yield very desirable results that are spatially consistent and well boundary located. It also benefits the segmentation of docked ships that are quite challenging for many previous methods. Experimental results on two datasets collected fromGoogleEarth have demonstrated the effectiveness of our approach both in quantitative and qualitative performance compared with state-of-the-art methods.
KeywordEdge Aware Regularization Harbor Images Multitask Learning Semantic Segmentation
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
DOI10.1109/JSTARS.2017.2747599
WOS KeywordINSHORE SHIP DETECTION ; NEURAL-NETWORK ; SATELLITE IMAGERY ; AERIAL IMAGES ; CLASSIFICATION ; EXTRACTION ; SHAPE ; INFORMATION ; FEATURES ; SALIENCY
Indexed BySCI
Language英语
Funding OrganizationNational 863 projects(2015AA042307) ; National Natural Science Foundation of China(91338202 ; Beijing Natural Science Foundation(4162064) ; 91438105 ; 91646207 ; 61370039)
WOS Research AreaEngineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEngineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000418871200036
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15515
Collection空天信息研究中心
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Cheng, Dongcai,Meng, Gaofeng,Xiang, Shiming,et al. FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2017,10(12):5769-5783.
APA Cheng, Dongcai,Meng, Gaofeng,Xiang, Shiming,&Pan, Chunhong.(2017).FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,10(12),5769-5783.
MLA Cheng, Dongcai,et al."FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 10.12(2017):5769-5783.
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