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Proceedings Paper

A novel model for edge aware sea-land segmentation
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Paper Abstract

Sea-land segmentation is one of important research domains in the remote sensing image processing. Edge aware of sealand segmentation is one of hot-points. Edge information is used as an auxiliary learning to provide more information for the segmentation. In this paper, we propose a novel model for the sea-land segmentation with an edge detection in the lower layers and segmentation in higher layers, which is proved as an effective way to fuse the different tasks. We exploit pre-trained VGG16 model to initial the backbone. We use F-score to assess the segment output. Land accuracy is 0.9929 of F-score and sea accuracy score is 0.9937 of F-score in our own test dataset in the sea-land segmentation, which is the highest score among the five methods we take in the comparisons.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114320I (14 February 2020); doi: 10.1117/12.2541732
Show Author Affiliations
Peng Gao, Huazhong Univ. of Science and Technology (China)
Jinwen Tian Sr., Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11432:
MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Zhiguo Cao; Jie Ma; Zhong Chen; Yu Shi, Editor(s)

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