Earth observation (EO), aiming at monitoring the state of planet Earth using remote sensing
data, is critical for improving our daily lives and living environment. With a growing number …

Water body classification from high-resolution optical remote sensing (RS) images, aiming at
classifying whether each pixel of the image is water or not, has become a hot issue in the …

Due to the light absorption and scattering induced by the water medium, underwater images
usually suffer from some degradation problems, such as low contrast, color distortion, and …

Focusing on the issue of how to effectively capture and utilize cross-modality information in
RGB-D salient object detection (SOD) task, we present a convolutional neural network …

Convolutional neural networks (CNNs) are good at extracting contexture features within
certain receptive fields, while transformers can model the global long-range dependency …

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in
natural scenes. Accurate COD suffers from a number of challenges associated with low …

High spatial resolution (HSR) remote sensing images contain complex foreground-
background relationships, which makes the remote sensing land cover segmentation a …

In this article, we deal with the problem of object detection on remote sensing images.
Previous researchers have developed numerous deep convolutional neural network (CNN) …

Optical remote sensing images (RSIs) have been widely used in many applications, and one
of the interesting issues about optical RSIs is the salient object detection (SOD). However …

Recently, relying on convolutional neural networks (CNNs), many methods for salient object
detection in optical remote-sensing images (ORSI-SOD) are proposed. However, most …