Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification

Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification

Hyperspectral image cross-scene classification (HSICC) tasks are confronted with tremendous challenges due to spectral shift phenomena across scenes and the tough work of obtaining labels. Unsupervised domain adaptation has proven its effectiveness in tackling this issue, but it has a fatal limitati...

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Main Authors: Xuebin Tang, Hanyi Shi, Chunchao Li, Cheng Jiang, Xiaoxiong Zhang, Lingbin Zeng, Xiaolei Zhou
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Remote Sensing
Subjects:
remote sensing
hyperspectral imaging cross-scene classification (HSICC)
few-shot unsupervised domain adaptation (FUDA)
refined bi-directional prototypical contrastive learning (RBPCL)
self-collected ultralow-altitude hyperspectral datasets
Online Access:https://www.mdpi.com/2072-4292/17/13/2305
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