Water body classification from high-resolution optical remote sensing (RS) images, aiming at
classifying whether each pixel of the image is water or not, has become a hot issue in the …

Large-scale high-resolution land-cover mapping is a way to comprehend the Earth's surface
and resolve the ecological and resource challenges facing humanity. High-resolution (≤ 1 …

The proposal of the segment anything model (SAM) has created a new paradigm for the
deep-learning-based semantic segmentation field and has shown amazing generalization …

As one of the most significant components of the ecosystem, waterbody needs to be highly
monitored at different spatial and temporal scales. Nevertheless, waterbody variations in …

Despite the extraordinary success of the deep architectures on semantic segmentation for
remote sensing (RS) images, they have difficulties in learning new classes from a sequential …

Global surface water detection in very-high-resolution (VHR) satellite imagery can directly
serve major applications such as refined flood mapping and water resource assessment …

Water body segmentation is an important tool for the hydrological monitoring of the Earth.
With the rapid development of convolutional neural networks, semantic segmentation …

At present, deep learning is a hot topic in the field of the classification of hyperspectral image
(HSI), and it has aroused wide attention. However, in fine-grained classification tasks, such …

Building change detection (CD) plays a crucial role in urban planning, land resource
management, and disaster monitoring. Currently, deep learning has become a key …

Segmentation incremental learning (SIL) has received a lot of attention in recent years due
to the ability to overcome the problem of catastrophic forgetting. Our study found that …