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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
2017-12-01
发表期刊IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷号10期号:12页码:5769-5783
文章类型Article
摘要Sea-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.
关键词Edge Aware Regularization Harbor Images Multitask Learning Semantic Segmentation
WOS标题词Science & Technology ; Technology ; Physical Sciences
DOI10.1109/JSTARS.2017.2747599
关键词[WOS]INSHORE SHIP DETECTION ; NEURAL-NETWORK ; SATELLITE IMAGERY ; AERIAL IMAGES ; CLASSIFICATION ; EXTRACTION ; SHAPE ; INFORMATION ; FEATURES ; SALIENCY
收录类别SCI
语种英语
项目资助者National 863 projects(2015AA042307) ; National Natural Science Foundation of China(91338202 ; Beijing Natural Science Foundation(4162064) ; 91438105 ; 91646207 ; 61370039)
WOS研究方向Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000418871200036
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15515
专题空天信息研究中心
作者单位Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
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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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