Remote sensing image scene classification with deep learning (DL) is a rapidly growing
field that has gained significant attention in the past few years. While previous review papers …

Large-scale vision foundation models have made significant progress in visual tasks on
natural images, with vision transformers (ViTs) being the primary choice due to their good …

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 …

Change detection of natural lake boundaries is one of the important tasks in remote sensing
image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons …

In recent years, remote-sensing scene classification is one of the research hotspots and has
played an important role in the field of intelligent interpretation of remote-sensing data …

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 …

Convolutional neural network (CNN)-based methods are widely used in remote sensing
image scene classification and can obtain excellent performances. However, the stacked …

Accurate classification of remote sensing (RS) images is a perennial topic of interest in the
RS community. Recently, transfer learning, especially for fine-tuning pretrained …

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 …

Remote sensing scene classification (RSSC) is a hotspot and play very important role in the
field of remote sensing image interpretation in recent years. With the recent development of …