Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks-Reference-Cited by-同舟云学术

Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks

Published:2023-12 Issue: Volume:299 Page:113856
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Hong Danfeng,Zhang Bing,Li Hao,Li Yuxuan,Yao Jing,Li Chenyu,Werner Martin,Chanussot Jocelyn,Zipf Alexander,Zhu Xiao Xiang

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference63 articles.

1. Deep learning in environmental remote sensing: achievements and challenges;Yuan;Remote Sens. Environ.,2020

2. A review of deep learning methods for semantic segmentation of remote sensing imagery;Yuan;Expert Syst. Appl.,2021

3. Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks;Kampffmeyer,2016

4. Dropout as a bayesian approximation: Representing model uncertainty in deep learning;Gal,2016

5. Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning;Kemker;ISPRS J. Photogramm. Remote Sens.,2018

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