Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …

Deep learning has largely reshaped remote sensing (RS) research for aerial image
understanding and made a great success. Nevertheless, most of the existing deep models …

Remote sensing images pose distinct challenges for downstream tasks due to their inherent
complexity. While a considerable amount of research has been dedicated to remote sensing …

With the ever-growing complexity of models in the field of remote sensing (RS), there is an
increasing demand for solutions that balance model accuracy with computational efficiency …

Hyperspectral object tracking is aimed at tracking targets by using both spatial information
and abundant spectral information, overcoming the drawbacks of traditional RGB tracking in …

In recent years, significant progress has been achieved in urban dense prediction tasks,
particularly with advancements in deep learning models and novel architectures that …

Due to the variation in spatial resolution and the diversity of object scales, the interpretation
of optical remote sensing images is extremely challenging. Deep learning has become the …

Currently, under supervised learning, a model pre-trained by a large-scale nature scene
dataset and then fine-tuned on a few specific task labeling data is the paradigm that has …

Remote sensing images have the essential attribute of large-scale spatial variation and
complex scene information, as well as the high similarity between various classes and the …

As a front-burner problem in incremental learning, class incremental semantic segmentation
(CISS) is plagued by catastrophic forgetting and semantic drift. Although recent methods …