FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images

doi:10.1109/JSTARS.2017.2747599

	FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images
	Cheng, Dongcai; Meng, Gaofeng; Xiang, Shiming; Pan, Chunhong
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2017-12-01
卷号	10 期号:12 页码:5769-5783
关键词	Edge Aware Regularization Harbor Images Multitask Learning Semantic Segmentation
DOI	10.1109/JSTARS.2017.2747599
文献子类	Article
英文摘要	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.
WOS关键词	INSHORE SHIP DETECTION ; NEURAL-NETWORK ; SATELLITE IMAGERY ; AERIAL IMAGES ; CLASSIFICATION ; EXTRACTION ; SHAPE ; INFORMATION ; FEATURES ; SALIENCY
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
WOS记录号	WOS:000418871200036
资助机构	National 863 projects(2015AA042307) ; National Natural Science Foundation of China(91338202 ; Beijing Natural Science Foundation(4162064) ; 91438105 ; 91646207 ; 61370039)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/15515]
专题	自动化研究所_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
推荐引用方式 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.