Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …

Researchers and engineers have increasingly used Deep Learning (DL) for a variety of
Remote Sensing (RS) tasks. However, data from local observations or via ground truth is …

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

The effective fusion of multisource data helps to improve the performance of land cover
classification. Most existing convolutional neural network (CNN)-based methods adopt an …

Recently, the scenes in large high-resolution remote sensing (HRRS) datasets have been
classified using convolutional neural network (CNN)-based methods. Such methods are well …

Recently, much research has shown that deep learning methods are superior in scene
classification for remote sensing images (HSIs). However, the lack of labelled samples and …

Self-supervised learning (SSL) has become the new state of the art in several domain
classification and segmentation tasks. One popular category of SSL are distillation networks …

Spectral unmixing expresses the mixed pixels existing in hyperspectral images as the
product of endmembers and their corresponding fractional abundances, which has been …

Remote sensing scene objective recognition (RSSOR) plays a serious application value in
both military and civilian fields. Convolutional neural networks (CNNs) have greatly …

Deep learning-based semantic segmentation has been widely applied for building
extraction. However, due to the domain gap, the extraction of building in high-resolution …