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SeNet: Structured Edge Network for Sea--Land Segmentation
Cheng, Dongcai; Meng, Gaofeng; Cheng, Guangliang; Pan, Chunhong
2017
发表期刊IEEE Geoscience and Remote Sensing Letters
期号2页码:247-251
摘要  Separating an optical remote sensing image into sea  and land areas is very challenging yet of great importance to the coastline extraction and subsequent object detection. Traditional methods based on handcrafted feature extraction and image processing often face dilemma when confronting high resolution remote sensing images for their complicated texture and intensity distribution. In this paper, we apply the prevalent deep convolution neural networks (CNN) to the sea--land segmentation problem and make two innovations on top of the traditional structure: firstly, we propose a local smooth regularization to achieve better spatially consistent results, which frees us from the complicated morphological operations that are commonly used in traditional methods; secondly, we use a multi-task loss to simultaneously obtain the segmentation and edge detection results. The attached structured edge detection branch can further refine the segmentation result and dramatically improve edge accuracy. Experiments  on a set of natural-colored images from Google Earth demonstrate the effectiveness of our approach in terms of quantitative and visual performances compared with state-of-the-art methods.
关键词Sea--land Segmentation Deconvolution Network (Deconvnet) Local Smooth Regularization Structured Edge Network (Senet)
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15514
专题空天信息研究中心
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Cheng, Dongcai,Meng, Gaofeng,Cheng, Guangliang,et al. SeNet: Structured Edge Network for Sea--Land Segmentation[J]. IEEE Geoscience and Remote Sensing Letters,2017(2):247-251.
APA Cheng, Dongcai,Meng, Gaofeng,Cheng, Guangliang,&Pan, Chunhong.(2017).SeNet: Structured Edge Network for Sea--Land Segmentation.IEEE Geoscience and Remote Sensing Letters(2),247-251.
MLA Cheng, Dongcai,et al."SeNet: Structured Edge Network for Sea--Land Segmentation".IEEE Geoscience and Remote Sensing Letters .2(2017):247-251.
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