SANet: A Sea–Land Segmentation Network Via Adaptive Multiscale Feature Learning

SANet: A Sea–Land Segmentation Network Via Adaptive Multiscale Feature Learning

Sea-land segmentation of remote sensing images is of great significance to the dynamic monitoring of coastlines. However, the types of objects in the coastal zone are complex, and their spectra, textures, shapes, and distribution features are different. Therefore, sea-land segmentation for various t...

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Bibliographic Details
Main Authors:	Binge Cui, Wei Jing, Ling Huang, Zhongrui Li, Yan Lu
Format:	Article
Language:	English
Published:	IEEE 2021-01-01
Series:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:	Adaptive learning atrous convolution remote sensing image residual block sea–land segmentation squeeze-excitation module
Online Access:	https://ieeexplore.ieee.org/document/9269403/

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