With the rapid development of the remote sensing monitoring and computer vision
technology, the deep learning method has made a great progress to achieve applications …

Reducing the feature distribution shift caused by the factor of visual-environment changes,
named visual-environment changes (VE-changes), is a hot issue in domain adaptation …

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 …

Semantic segmentation for remote sensing images (RSI) is critical for the Earth monitoring
system. However, the covariate shift between RSI datasets under different capture …

Unsupervised domain adaptation (UDA) is an important solution to reduce the bias between
the labeled source domain and the unlabeled target domain. It has attracted more attention …

Sea–land segmentation of remote sensing images is of great significance to the dynamic
monitoring of coastlines. However, the types of objects in the coastal zone are complex, and …

The performance of a semantic segmentation model for remote sensing (RS) images pre-
trained on an annotated dataset greatly decreases when testing on another unannotated …

Cloud occlusion phenomena are widespread in optical remote sensing (RS) images,
leading to information loss and image degradation and causing difficulties in subsequent …

Deep learning-based semantic segmentation has made great progress in understanding
very-high-resolution (VHR) remote sensing images (RSIs). However, large-scale …

Convolutional neural networks (CNNs) have achieved tremendous success in computer
vision tasks, such as building extraction. However, due to domain shift, the performance of …