Zero-shot remote sensing scene classification aims to solve the scene classification problem
on unseen categories and has attracted numerous research attention in the remote sensing …

Few-shot learning is an important and challenging research topic for remote sensing image
scene classification. Many existing approaches address this challenge by using meta …

Deep neural network has been successfully applied to remote sensing image scene
classification, which requires a large amount of annotated data for training. However, it is …

Self-supervised learning is an effective way to solve model collapse for few-shot remote
sensing scene classification (FSRSSC). However, most self-supervised contrastive learning …

Few-shot remote sensing image scene classification (FS-RSISC) aims at classifying remote
sensing images with only a few labeled samples. The main challenges lie in small interclass …

The few-shot scene classification is dedicated to identifying unseen remote sensing classes
when only a very small number of labeled samples are available for reference. Most of the …

Hyperspectral image (HSI) classification methods have made great progress in recent years.
However, most of these methods are rooted in the closed-set assumption that the class …

Equipping a deep model the ability of few-shot learning (FSL) is a core challenge for artificial
intelligence. Gradient-based meta-learning effectively addresses the challenge by learning …

Few-shot remote sensing scene classification (RSSC) is of high practical value in real
situations where data are scarce and annotated costly. The few-shot learner needs to …

As a content management technique, remote sensing (RS) scene classification (RSSC)
always attracts researchers' attention. In the past decades, many successful methods have …