FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images | |
Cheng, Dongcai; Meng, Gaofeng; Xiang, Shiming; Pan, Chunhong | |
发表期刊 | 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 |
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 |
DOI | 10.1109/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 | |
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 |
第一作者单位 | 模式识别国家重点实验室 |
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. |
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