Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …

Segmentation and classification are two imperative, yet challenging tasks in image analysis
for remote-sensing applications. In the former, an image is divided into spatially continuous …

Rainfall reservoir-induced landslides in the Zigui Basin, China Three Gorges Reservoir
(CTGR) area, exhibit typical step-like deformation characteristics with mutation and creep …

Abstract Domain adaptation techniques have attracted great attention in mechanical fault
diagnosis. However, most existing methods work under the assumption that the source and …

Domain adaptation, which aims at learning an accurate classifier for a new domain (target
domain) using labeled information from an old domain (source domain), has shown …

Class-wise adversarial adaptation networks are investigated for the classification of
hyperspectral remote sensing images in this article. By adversarial learning between the …

A new domain adaptation algorithm based on the class centroid and covariance alignment
(CCCA) is proposed for classification of remote sensing images. This approach exploits both …

In this article, we propose a novel deep domain adaptation method based on graph neural
network (GNN) for multitemporal hyperspectral remote sensing images. In GNN, graphs are …

Monitoring the vegetation structure and species composition of forest restoration (FR) in the
Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts …

Multimodally distributed data is very common in remote sensing images, such as
hyperspectral images (HSIs). It is important to capture the local manifold structure while …