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SeNet: Structured Edge Network for Sea-Land Segmentation
Jul 24, 2017Author:
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Title: SeNet: Structured Edge Network for Sea-Land Segmentation

 Authors: Cheng, DC; Meng, GF; Cheng, GL; Pan, CH

 Author Full Names: Cheng, Dongcai; Meng, Gaofeng; Cheng, Guangliang; Pan, Chunhong

 Source: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 14 (2):247-251; 10.1109/LGRS.2016.2637439 FEB 2017

 Language: English

 Abstract: Separating an optical remote sensing image into sea and land areas is very challenging yet of great importance to coastline extraction and subsequent object detection. Traditional methods based on handcrafted feature extraction and image processing often face this dilemma when confronting high-resolution remote sensing images for their complicated texture and intensity distribution. In this letter, we apply the prevalent deep convolutional neural networks to the sea-land segmentation problem and make two innovations on top of the traditional structure. First, 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. Second, we use a multitask 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. 

ISSN: 1545-598X

 eISSN: 1558-0571

 IDS Number: EM6NQ

 Unique ID: WOS:000395429800023

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