Learning and Adapting Robust Features for Satellite Image Segmentation on Heterogeneous Data Sets-Reference-Cited by-同舟云学术

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Learning and Adapting Robust Features for Satellite Image Segmentation on Heterogeneous Data Sets

Published:2019-09 Issue:9 Volume:57 Page:6517-6529
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Ghassemi Sina^ORCID,Fiandrotti Attilio,Francini Gianluca^ORCID,Magli Enrico^ORCID

Funder

TIM

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/8816706/08693644.pdf?arnumber=8693644

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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2. Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets;Aerospace;2023-10-12

3. A Comprehensive Review on Segmentation Techniques for Satellite Images;Archives of Computational Methods in Engineering;2023-05-22

4. Cross-domain landslide mapping from large-scale remote sensing images using prototype-guided domain-aware progressive representation learning;ISPRS Journal of Photogrammetry and Remote Sensing;2023-03

5. Large-scale deep learning based binary and semantic change detection in ultra high resolution remote sensing imagery: From benchmark datasets to urban application;ISPRS Journal of Photogrammetry and Remote Sensing;2022-11

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