An attention-fused network for semantic segmentation of very-high-resolution remote sensing imagery-Reference-Cited by-同舟云学术

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An attention-fused network for semantic segmentation of very-high-resolution remote sensing imagery

Published:2021-07 Issue: Volume:177 Page:238-262
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Yang Xuan,Li Shanshan,Chen Zhengchao,Chanussot Jocelyn,Jia Xiuping,Zhang Bing,Li Baipeng,Chen Pan

Funder

Chinese Academy of Sciences

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference60 articles.

1. Semantic segmentation of earth observation data using multimodal and multi-scale deep networks;Audebert,2016

2. Beyond rgb: Very high resolution urban remote sensing with multimodal deep networks;Audebert;ISPRS Journal of Photogrammetry and Remote Sensing,2018

3. Segnet: A deep convolutional encoder-decoder architecture for image segmentation;Badrinarayanan;IEEE transactions on pattern analysis and machine intelligence,2017

4. Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community;Ball;J. Appl. Remote Sens.,2017

5. Very high-resolution remote sensing: Challenges and opportunities [point of view];Benediktsson;Proc. IEEE,2012

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