Classification of land use and land cover from remote sensing images has been widely used
in natural resources and urban information management. The variability and complex …

Abstract Recently, Convolutional Neural Networks (CNN) methods achieved impressive
success in semantic segmentation tasks. However, challenges like class imbalance around …

Recent advancements in deep learning have spurred the development of numerous novel
semantic segmentation models for land cover mapping, showcasing exceptional …

The classification of urban land-use information has become the underlying database for a
variety of applications including urban planning and administration. The lack of datasets and …

Semantic segmentation of high-resolution remote sensing images plays an important role in
the remote sensing community. However, many indistinguishable objects are prevalent …

The more detailed and accurate earth observation has been made driven by the progress of
satellites and sensors optical photography technology, which poses both an opportunity and …

In recent years, small-scale drones have been used in heterogeneous tasks, such as border
control, precision agriculture, and search and rescue. This is mainly due to their small size …

A sparse label-oriented semantic segmentation network (SL-SSNet) is proposed for the
classification of hyperspectral images (HSIs) in this letter. Since semantic segmentation …

Semantic segmentation is a fundamental but meaningful task in the remote sensing image
understanding community. Great progress has been made in optical sensor photography …

The progress in optical remote sensing technology presents both a possibility and challenge
for small object segmentation task. However, the gap between human vision cognition and …