Remote sensing image scene classification (RSI-SC) is crucial for various high-level
applications, including RSI retrieval, image captioning, and object detection. Deep learning …

Semantic labelling of remote sensing images, technically termed as remote sensing scene
classification, plays significant role in understanding huge volume of complex remote …

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 …

Deep learning has achieved excellent performance in remote-sensing image scene
classification, since a large number of datasets with annotations can be applied for training …

Although machine learning classifiers have been successfully used in the medical and
engineering fields, there is still room for improving the predictive accuracy of model …

At present, few-shot object detection research in the field of optical remote sensing images
has been conducted, but few-shot object detection in the field of synthetic aperture radar …

Few-shot remote sensing scene classification (FSRSSC) is an important task, which aims to
recognize novel scene classes with few examples. Recently, several studies attempt to …

Convolutional neural networks (CNNs), in particular, demonstrate the remarkable power of
feature learning in remote sensing for land use and cover classification, as demonstrated by …