Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a
category to each pixel in remote sensing images, plays a vital role in a broad range of …

High-resolution remotely sensed images pose challenges to traditional semantic
segmentation networks, such as Convolutional Neural Networks (CNNs) and Vision …

Deep learning methods, especially convolutional neural networks (CNNs) and vision
transformers (ViTs), are frequently employed to perform semantic segmentation of high …

Few-shot segmentation (FSS) endeavors to utilize a minimal amount of annotated samples
(support) to guide the segmentation of unseen objects (query). Previous techniques …

Recent advancements in autoregressive networks with linear complexity have driven
significant research progress, demonstrating exceptional performance in large language …

Pansharpening is a process of fusing a high spatial resolution panchromatic image and a
low spatial resolution multispectral (MS) image to create a high-resolution MS image. A …

Remote sensing semantic segmentation tasks aim to automatically extract land cover types
by accurately classifying each pixel. However, large-scale hyperspectral remote sensing …

Due to variable imaging conditions, resolution degradation often occurs in aerial images,
which in turn impairs the performance upper bound of semantic segmentation (SS). To solve …

Semantic segmentation of aerial images is crucial in various practical applications,
encompassing traffic management, search tasks, urban planning, and more. However, due …

Convolutional neural networks have made significant progress in remote sensing image
processing. Convolutional networks mostly model the local information of samples based on …