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 …

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 …

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 …

Recently, many researchers have been dedicated to using convolutional neural networks
(CNNs) to extract global-context features (GCFs) for remote-sensing scene classification …

Learning powerful discriminative features is the key for remote sensing scene classification.
Most existing approaches based on convolutional neural network (CNN) have achieved …

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 …

More recently, remote sensing image classification has been moving from pixel-level
interpretation to scene-level semantic understanding, which aims to label each scene image …

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 …

Remote sensing image scene classification is a fundamental problem, which aims to label
an image with a specific semantic category automatically. Recent progress on remote …