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 …

Remote sensing image semantic segmentation (RSISS) remains challenging due to the
scarcity of labeled data. Semisupervised learning can leverage pseudolabels to enhance …

Among the most difficult difficulties in contemporary satellite image-processing subjects is
cloud and cloud shade segmentation. Due to substantial background noise interference …

Deep learning has become a popular method for studying the semantic segmentation of
high-resolution remote sensing images (HRRSIs). Existing methods have adopted …

Concerning the ever-changing wetland environment, the efficient extraction of wetland
information holds great significance for the research and management of wetland …

Sea–land segmentation of optical remote-sensing images holds great importance for military
and civilian applications, such as coastal monitoring, target detection, and resource …

Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing
images (RSIs). However, conventional methods predominantly focus on learning …

Automatically extracting water bodies is a significant task in interpreting remote sensing
images (RSIs). Convolutional neural networks (CNNs) have exhibited excellent performance …

Water body extraction is a typical task in the semantic segmentation of remote sensing
images (RSIs). Deep convolutional neural networks (DCNNs) outperform traditional …

Aerial Image Semantic segmentation based on convolution neural networks (CNNs) has
made significant process in recent years. Nevertheless, their vulnerability to adversarial …