FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images
文献类型：期刊论文
| 作者 | Cheng, Dongcai ; Meng, Gaofeng; Xiang, Shiming; Pan, Chunhong
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| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 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) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/15515]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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