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 …

We explore the use of convolutional neural networks for the semantic classification of remote
sensing scenes. Two recently proposed architectures, CaffeNet and GoogLeNet, are …

Feature extraction is vital for face recognition. In this paper, we focus on the general feature
extraction framework for robust face recognition. We collect about 300 papers regarding face …

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

Recognising remote sensing scene images remains challenging due to large visual-
semantic discrepancies. These mainly arise due to the lack of detailed annotations that can …

Recent progress on remote sensing (RS) scene classification is substantial, benefiting
mostly from the explosive development of convolutional neural networks (CNNs). However …

Land scene classification in satellite imagery has a wide range of applications in remote
surveillance, environment monitoring, remote scene analysis, Earth observations and urban …

The existing convolutional neural network (CNN) models have shown excellent performance
in remote sensing scene classification. However, the structure of such models is becoming …

Convolutional neural networks (CNNs) have recently emerged as a popular topic for
machine learning in various academic and industrial fields. It is often an important problem …

One of the challenging problems in understanding high‐resolution remote sensing images
is aerial scene classification. A well‐designed feature representation method and classifier …