A novel model for edge aware sea-land segmentation

Peng Gao; Jinwen Tian Sr.

doi:10.1117/12.2541732

14 February 2020 A novel model for edge aware sea-land segmentation

Peng Gao, Jinwen Tian Sr.

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 114320I (2020) https://doi.org/10.1117/12.2541732
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Peng Gao and Jinwen Tian Sr. "A novel model for edge aware sea-land segmentation", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114320I (14 February 2020); https://doi.org/10.1117/12.2541732

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KEYWORDS

Image segmentation

Edge detection

Data modeling

Statistical modeling

Convolution

Feature extraction

Image processing

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