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 …

The existing deep networks have shown excellent performance in remote sensing scene
classification (RSSC), which generally requires a large amount of class-balanced training …

Remote sensing scene understanding is a highly challenging task, and has gradually
emerged as a research hotspot in the field of intelligent interpretation of remote sensing …

In remotely sensed images, high intraclass variance and interclass similarity are ubiquitous
due to complex scenes and objects with multivariate features, making semantic …

The foundation model (FM) has garnered significant attention for its remarkable transfer
performance in downstream tasks. Typically, it undergoes task-agnostic pretraining on a …

With the development of high-resolution satellites, more and more attention has been paid to
remote sensing (RS) scene classification. Convolutional neural networks (CNNs), which …

Cross-modal remote-sensing image–text retrieval (CMRSITR) is a challenging topic in the
remote-sensing (RS) community. It has gained growing attention because it can be flexibly …

Significant progress has been achieved in remote sensing image scene classification
(RSISC) with the development of convolutional neural networks (CNNs) and vision …

Deep convolutional neural networks (CNNs) are significant in remote sensing. Due to the
strong local representation learning ability, CNNs have excellent performance in remote …

Convolution and self-attention are two powerful techniques for multisource remote sensing
(RS) data fusion that have been widely adopted in Earth observation tasks. However …