Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …

With the introduction of Earth observation satellites, the classification technology through
high-definition remote sensing images appeared. After decades of evolution, the land cover …

The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data
being produced at the edge. The integration of self-supervised learning (SSL) and federated …

In the application of remote sensing, cloud blocking brings trouble to the analysis of surface
parameters and atmospheric parameters. Due to the complexity of the background, the …

The use of remote sensing images for land cover analysis has broad prospects. At present,
the resolution of aerial remote sensing images is getting higher and higher, and the span of …

Cloud and cloud shadow segmentation is one of the most important issues in remote
sensing image processing. Most of the remote sensing images are very complicated. In this …

With the increasing demand of autonomous systems, pixelwise semantic segmentation for
visual scene understanding needs to be not only accurate but also efficient for potential real …

Extracting mask information of buildings and water areas from high resolution remote
sensing images is beneficial to monitoring and management of urban development …

Advances in machine learning technology have enabled real-time extraction of semantic
information in signals which can revolutionize signal processing techniques and improve …

Extracting buildings and water bodies from high-resolution remote sensing images is of
great significance for urban development planning. However, when studying buildings and …