Although enormous domain adaptation (DA) approaches have been proposed for cross-
scene hyperspectral image (HSI) classification, the majority of DA methods strongly depend …

Annotating remote sensing images (RSIs) poses a significant challenge, primarily due to its
labor-intensive nature. Semi-supervised object detection (SSOD) methods address this …

Recently, weak-to-strong consistency-based methods have yielded a remarkable
performance for remote sensing image segmentation. However, they are designed within a …

Existing deep models have greatly enhanced the performance of semantic segmentation in
remote sensing (RS) images, but they are often limited by the scarcity of labeled samples …

Influenced by deep learning, extracting roads from high-resolution remote sensing images
has attracted extensive attention. However, most previous works have focused on fully …

Accurate extraction of building footprints from satellite imagery is of high value. Currently,
deep learning methods are predominant in this field due to their powerful representation …

In Earth observation, semantic understanding of Remote Sensing (RS) images holds
significant importance, yet it is hindered in practice by the need for extensive manual pixel …

Semi-supervised semantic segmentation has gained significant attention as a method to
reduce the substantial expense associated with pixel-level labeling. The existing methods …

Semisupervised change detection (CD) methods have garnered increasing attention due to
their capacity to alleviate the dependency of fully-supervised methods on a large number of …

In the art of fabric design, the technic of image recolorization is usually used to generate
synthetic fabric images that can serve as new fabric design proposals. However, classical …