Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …

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 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 …

Scene classification is a highly useful task in Remote Sensing (RS) applications. Many
efforts have been made to improve the accuracy of RS scene classification. Scene …

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 is mainly used to investigate sites of dams, bridges, and pipelines to locate
construction materials and provide detailed geographic information. In remote sensing …

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 …