The past years have witnessed great progress on remote sensing (RS) image interpretation
and its wide applications. With RS images becoming more accessible than ever before …

Aerial scene classification, which aims to automatically label an aerial image with a specific
semantic category, is a fundamental problem for understanding high-resolution remote …

Remote sensing image scene classification is a fundamental problem, which aims to label
an image with a specific semantic category automatically. Recently, deep learning methods …

Remote sensing image scene classification plays an important role in a wide range of
applications and hence has been receiving remarkable attention. During the past years …

Classifying different satellite remote sensing scenes is a very important subtask in the field of
remote sensing image interpretation. With the recent development of convolutional neural …

Scene classification is one of the most important applications of remote sensing.
Researchers have proposed various datasets and innovative methods for remote sensing …

This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2
benchmark archive. The BigEarthNet consists of 590, 326 Sentinel-2 image patches, each of …

High resolution remote sensing (HRRS) image scene classification plays a crucial role in a
wide range of applications and has been receiving significant attention. Recently …

The rapid development of high spatial resolution (HSR) remote sensing imagery techniques
not only provide a considerable amount of datasets for scene classification tasks but also …

Remote sensing (RS) scene classification plays an important role in a wide range of RS
applications. Recently, convolutional neural networks (CNNs) have been applied to the field …