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

As an important application in remote sensing, landcover classification remains one of the
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …

Conventional deep CNN-based segmentation approaches have achieved satisfactory
performance in recent years, however, they are essentially Big Data-driven technologies …

Few-shot learning brings the machine close to human thinking which enables fast learning
with limited samples. Recent work considers local features to achieve contextual semantic …

Deep learning (DL) has been extensively used for hyperspectral image classification (HSIC)
with significant success, but the classification of high-dimensional hyperspectral image (HSI) …

In recent years, convolutional neural network (CNN)-based methods have been widely used
for remote sensing (RS) scene classification tasks and have achieved excellent results …

In recent years, few-shot segmentation (FSS) has received widespread attention from
scholars by virtue of its superiority in low-data regimes. Most existing research focuses on …

Oriented object detection (OOD) aims to precisely detect the objects with arbitrary orientation
in remote sensing images (RSIs). Up to now, most of the bounding box regression (BBR) …

Deep convolutional neural networks have become an indispensable method in remote
sensing image scene classification because of their powerful feature extraction capabilities …

Few-shot segmentation (FSS), which aims to determine specific objects in the query image
given only a handful of densely labeled samples, has received extensive academic attention …