Detecting the salient objects in a remote sensing image has wide applications. Many
existing deep learning methods have been proposed for Salient Object Detection (SOD) in …

Cross-scene classification focuses on setting up an effective domain adaptation (DA) way to
transfer the learnable knowledge from source to target domain, which can be reasonably …

Cloud detection in high-resolution remote sensing images (HRSIs) is widely applied to cross-
spatiotemporal domains with various scenarios change. However, cloud detection semantic …

The self-supervised learning (SSL) technique, driven by massive unlabeled data, is
expected to be a promising solution for semantic segmentation of remote sensing images …

The ability to precisely identify the semantic categories of low-resolution (LR) aerial images
is a critical skill in the field of remote sensing. However, accurately performing this task is …

Object detection in UAV aerial images faces domain-adaptive challenges, such as changes
in shooting height, viewing angle, and weather. These changes constitute a large number of …

Current land use classification models based on very high-resolution (VHR) remote sensing
images often suffer from high sample dependence and poor transferability. To address these …

The process of estimating and counting tree density using only a single aerial or satellite
image is a difficult task in the fields of photogrammetry and remote sensing. However, it …

There are hundreds of high-and low-altitude earth observation satellites that asynchronously
capture massive-scale aerial photographs every day. Generally, high-altitude satellites take …

Cross-domain semantic segmentation of remote sensing (RS) imagery based on
unsupervised domain adaptation (UDA) techniques has significantly advanced deep …