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

Images gathered from different satellites are vastly available these days due to the fast
development of remote sensing (RS) technology. These images significantly enhance the …

Transformer architectures have become state-of-the-art models in computer vision and
natural language processing. To a significant degree, their success can be attributed to self …

Transformer models have recently approached or even surpassed the performance of
ConvNets on computer vision tasks like classification and segmentation. To a large degree …

Self-supervised pretraining bears the potential to generate expressive representations from
large-scale Earth observation (EO) data without human annotation. However, most existing …

Labeled samples are important in achieving land cover change detection (LCCD) tasks via
deep learning techniques with remote sensing images. However, labeling samples for …

Self-supervised learning (SSL) has gained wide-spread attention in the remote sensing (RS)
and Earth observation (EO) communities owing to its ability to learn task-agnostic …

About half of all optical observations collected via spaceborne satellites are affected by haze
or clouds. Consequently, cloud coverage affects the remote-sensing practitioner's …

Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …

Flood events are often accompanied by rainy weather, which limits the applicability of optical
satellite images, whereas synthetic aperture radar (SAR) is less sensitive to weather and …