With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

Change detection based on remote sensing images plays an important role in the field of
remote sensing analysis, and it has been widely used in many areas, such as resources …

Most domain adaptation (DA) methods in cross-scene hyperspectral image classification
focus on cases where source data (SD) and target data (TD) with the same classes are …

With the development of the sensor technology, complementary data of different sources can
be easily obtained for various applications. Despite the availability of adequate multisource …

Classification and identification of the materials lying over or beneath the earth's surface
have long been a fundamental but challenging research topic in geoscience and remote …

With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …

Deep-learning-based methods are widely used in multisource remote-sensing image
classification, and the improvement in their performance confirms the effectiveness of deep …

Domain adaptation techniques have been widely applied to the problem of cross-scene
hyperspectral image (HSI) classification. Most existing methods use convolutional neural …

The joint use of multisource remote-sensing (RS) data for Earth observation missions has
drawn much attention. Although the fusion of several data sources can improve the accuracy …

Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …