Self-training guided disentangled adaptation for cross-domain remote sensing image semantic segmentation

Self-training guided disentangled adaptation for cross-domain remote sensing image semantic segmentation

Remote sensing (RS) image semantic segmentation using deep convolutional neural networks (DCNNs) has shown great success in various applications. However, the high dependence on annotated data makes it challenging for DCNNs to adapt to different RS scenes. To address this challenge, we propose a cro...

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Bibliographic Details
Main Authors: Qi Zhao, Shuchang Lyu, Hongbo Zhao, Binghao Liu, Lijiang Chen, Guangliang Cheng
Format: Article
Language:English
Published: Elsevier 2024-03-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Remote sensing image semantic segmentation
Unsupervised domain adaptation
Self-training
Domain disentangling
Adversarial learning
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843223004703

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http://www.sciencedirect.com/science/article/pii/S1569843223004703

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