Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …

In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …

Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

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 …

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing
image analysis over the past few years. In this study, the major DL concepts pertinent to …

Hyperspectral image (HSI) classification has become a hot topic in the field of remote
sensing. In general, the complex characteristics of hyperspectral data make the accurate …

Availability of very high-resolution remote sensing images and advancement of deep
learning methods have shifted the paradigm of image classification from pixel-based and …

Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …

Machine learning, particularly deep learning (DL), has become a central and state-of-the-art
method for several computer vision applications and remote sensing (RS) image …

Landslide, one of the most critical natural hazards, is caused due to specific compositional
slope movement. In the past decades, due to inflation of urbanized area and climate change …