Weakly supervised high spatial resolution land cover mapping based on self-training with weighted pseudo-labels

Weakly supervised high spatial resolution land cover mapping based on self-training with weighted pseudo-labels

Despite its success, deep learning in land cover mapping requires a massive amount of pixel-wise labeled images. It typically assumes that the training and test scenes are similar in data distribution. The performance of models trained on any particular dataset could degrade significantly on a new d...

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Bibliographic Details
Main Authors: Wei Liu, Jiawei Liu, Zhipeng Luo, Hongbin Zhang, Kyle Gao, Jonathan Li
Format: Article
Language:English
Published: Elsevier 2022-08-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Unsupervised domain adaptation
Land cover mapping
Self-training
Pseudo-learning
Semantic segmentation
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843222001297

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http://www.sciencedirect.com/science/article/pii/S1569843222001297

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