Convolutional neural networks (CNNs) and Transformers have made impressive progress in
the field of remote sensing change detection (CD). However, both architectures have …

Context modeling is critical for remote sensing image dense prediction tasks. Nowadays, the
growing size of very-high-resolution (VHR) remote sensing images poses challenges in …

Remote sensing images pose distinct challenges for downstream tasks due to their inherent
complexity. While a considerable amount of research has been dedicated to remote sensing …

Foundation models have reshaped the landscape of remote sensing (RS) by enhancing
various image interpretation tasks. Pretraining is an active research topic, encompassing …

The rapid advancement of automated artificial intelligence algorithms and remote sensing
instruments has benefited change detection (CD) tasks. However, there is still a lot of space …

Recently, deep learning (DL) models have become the main focus for the remote sensing
change detection tasks. Numerous publications on supervised and unsupervised DL-based …

A high-precision feature extraction model is crucial for change detection (CD). In the past,
many deep learning-based supervised CD methods learned to recognize change feature …

Hyperspectral imaging technology produces images that capture both spatial and spectral
details of features, constituting a key data source in remote sensing detection. However, the …

Recent years have witnessed the superiority of deep learning-based algorithms in the field
of HSI classification. However, a prerequisite for the favorable performance of these …

Cropland non-agriculturalization (CNA) refers to the conversion of cropland into construction
land, woodland/garden/grassland, water body, or other non-agricultural land, which …