In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …

Global context information is essential for the semantic segmentation of remote sensing (RS)
images. However, most existing methods rely on a convolutional neural network (CNN) …

This article presents a transformer and convolutional neural network (CNN) hybrid deep
neural network for semantic segmentation of very high resolution (VHR) remote sensing …

The trade-off between feature representation power and spatial localization accuracy is
crucial for the dense classification/semantic segmentation of remote sensing images (RSIs) …

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 …

Despite the application of state-of-the-art fully Convolutional Neural Networks (CNNs) for
semantic segmentation of very high-resolution optical imagery, their capacity has not yet …

Parsing very high resolution (VHR) urban scene images into regions with semantic
meaning, eg buildings and cars, is a fundamental task in urban scene understanding …

The semantic segmentation of remote sensing images (RSIs) is important in a variety of
applications. Conventional encoder-decoder-based convolutional neural networks (CNNs) …

Semantic segmentation of remote sensing imagery plays a pivotal role in extracting precise
information for diverse downstream applications. Recent development of the segment …

Capturing accurate multiscale semantic information from the images is of great importance
for high-quality semantic segmentation. Over the past years, a large number of methods …