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

With many platforms and sensors continuously observing the earth surface, the large
amount of remote sensing data presents a big data challenge. While remote sensing data …

The domain adaptation (DA) approaches available to date are usually not well suited for
practical DA scenarios of remote sensing image classification since these methods (such as …

The detection of early signs of volcanic unrest preceding an eruption in the form of ground
deformation in interferometric synthetic aperture radar (InSAR) data is critical for assessing …

ABSTRACT Information on Land Use and Land Cover Map (LULCM) is essential for
environment and socioeconomic applications. Such maps are generally derived from …

The field of scene recognition, which lies at the crossroads of computer vision and artificial
intelligence, has experienced notable progress because of scholarly pursuits. This article …

Remote Sensing categorical signature classification has gained significant implications on
spatial resolution image analysis due to differences in the sensors' spatial response and …

Scene-level change detection (SLCD), as opposed to pixel-level or object-level detection,
offers semantic-level change information, which is vital for monitoring land use changes. The …

In recent years, there is a growing interest in domain adaptation for remote sensing image
scene classification, particularly in universal domain adaptation, where both source and …

Multilabel remote sensing image classification is a fundamental task that classifies multiple
objects and land covers within an image. However, training deep learning models for this …