Enhancing Semi-Supervised Semantic Segmentation of Remote Sensing Images via Feature Perturbation-Based Consistency Regularization Methods

In the field of remote sensing technology, the semantic segmentation of remote sensing images carries substantial importance. The creation of high-quality models for this task calls for an extensive collection of image data. However, the manual annotation of these images can be both time-consuming a...

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書誌詳細

出版年:	Sensors
主要な著者:	Yi Xin, Zide Fan, Xiyu Qi, Ying Geng, Xinming Li
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2024-01-01
主題:	remote sensing semantic segmentation semi-supervised learning consistency regularization feature perturbation contrastive learning
オンライン･アクセス:	https://www.mdpi.com/1424-8220/24/3/730