Self-Training Based Image–Text Multimodal Unsupervised Domain Adaptation Segmentation Model for Remote Sensing Images

Deep self-training-based unsupervised domain adaptation (UDA) semantic segmentation methods learn from labeled source domain images and unlabeled target domain images, performing more stably than those based on adversarial training. We propose a self-training-based image–text multimodal unsupervised...

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Auteurs principaux:	Qianqian Liu, Xili Wang
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2026-02-01
Collection:	Remote Sensing
Sujets:	remote sensing images semantic segmentation unsupervised domain adaptation image–text multimodal self-training
Accès en ligne:	https://www.mdpi.com/2072-4292/18/4/651
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