Residual channel attention based sample adaptation few-shot learning for hyperspectral image classification

Residual channel attention based sample adaptation few-shot learning for hyperspectral image classification

Abstract Few-shot learning (FSL) uses prior knowledge and supervised experience to effectively classify hyperspectral images (HSIs), thereby reducing the cost of large numbers of labeled samples. However, existing few-shot methods ignore the correlation between cross-domain feature channels, and the...

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Bibliographic Details
Main Authors:	Yuefeng Zhao, Jingqi Sun, Nannan Hu, Chengmin Zai, Yanwei Han
Format:	Article
Language:	English
Published:	Nature Portfolio 2024-11-01
Series:	Scientific Reports
Online Access:	https://doi.org/10.1038/s41598-024-77747-2
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