Few-shot learning (FSL) has rapidly advanced in the hyperspectral image classification
(HSIC), potentially reducing the need for laborious and expensive labeled data collection …

Spectral shifts between source and target domains (TDs) pose significant challenges in
cross-domain hyperspectral image classification (HSIC). Current methods often struggle to …

Hyperspectral image (HSI) classification plays an important role in the field of remote
sensing. Even though we can easily acquire hyperspectral remote sensing images …

In hyperspectral image (HSI) classification scenarios, deep learning-based methods have
achieved excellent classification performance, but often rely on large-scale training datasets …

With the development of observation technology, multispectral (MS) images of large scenes
are easy to obtain, but the low spectral resolution limits their classification ability. Moreover …

Hyperspectral image (HSI) classification is a key technique in remote sensing. Despite the
increasing availability of high-quality HSI data, obtaining a large number of labeled samples …

Recently, few-shot learning based on meta-learning has shown great potential in
hyperspectral image classification (HSIC) due to its excellent adaptability to limited training …

In recent years, with the continuous development of deep learning (DL), neural networks
have demonstrated good results in large-sample hyperspectral image (HSI) classification …

Recently, metric-based few-shot learning (FSL) methods have achieved good performance
in the hyperspectral image (HSI) classification. However, existing methods suffer from two …

In the past few years, there has been significant progress in hyperspectral image
classification (HSIC). However, when the trained classifier on the source scene is directly …