Remote sensing (RS) scene classification plays an important role in the field of earth
observation. With the rapid development of the RS techniques, a large number of RS scene …

Remote sensing image scene classification refers to assigning semantic labels according to
the content of the remote sensing scenes. Most machine learning-based scene classification …

Scene classification from remote sensing images provides new possibilities for potential
application of high spatial resolution imagery. How to efficiently implement scene …

Although domain adaptation approaches have been proposed to tackle cross-regional,
multitemporal, and multisensor remote sensing applications since they do not require any …

Remote sensing scene classification (RSSC) refers to inferring semantic labels based on the
content of the remote sensing scenes. Recently, most works take the pretrained …

Convolutional neural networks (CNN) have attracted tremendous attention in the remote
sensing community due to its excellent performance in different domains. Especially for …

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

Fine-tuning of pretrained convolutional neural networks (CNNs) has been proven to be an
effective strategy for remote sensing image scene classification, particularly when a limited …

The scene classification of high spatial resolution (HSR) imagery involves labeling an HSR
image with a specific high-level semantic class according to the composition of the semantic …

Deep convolutional neural networks have become an indispensable method in remote
sensing image scene classification because of their powerful feature extraction capabilities …