Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Cross-modal remote sensing retrieval (RSCR) has an increasing importance due to the
ability to quickly and flexibly retrieve valuable data from enormous remote sensing (RS) …

Remote sensing (RS) image scene classification is an important research topic in the RS
community, which aims to assign the semantics to the land covers. Recently, due to the …

In recent years, convolutional neural network (CNN)-based methods have been widely used
for remote sensing (RS) scene classification tasks and have achieved excellent results …

Change detection (CD) is a hot research topic in the remote-sensing (RS) community. With
the increasing availability of high-resolution (HR) RS images, there is a growing demand for …

With the explosive growth of the volume and resolution of high-resolution remote-sensing
(HRRS) images, the management of them becomes a challenging task. The traditional …

The scene classification of remote sensing (RS) images plays an essential role in the RS
community, aiming to assign the semantics to different RS scenes. With the increase of …

Hyperspectral anomaly detection is aimed at detecting observations that differ from their
surroundings. To achieve this goal, low-rank models and autoencoders (AEs) have attracted …

Remote sensing image retrieval (RSIR) aims to search and retrieve the images of interest
from a large remote sensing image archive, which has remained to be a hot topic over the …

Remote sensing (RS) scene classification plays an essential role in the RS community and
has attracted increasing attention due to its wide applications. Recently, benefiting from the …