In recent times, many efforts have been made to improve remote sensing image scene
classification, especially using popular deep convolutional neural networks. However, most …

Remote sensing image scene classification (RSISC) is an active task in the remote sensing
community and has attracted great attention due to its wide applications. Recently, the deep …

The accuracy of remote sensing image segmentation and classification is known to
dramatically decrease when the source and target images are from different sources; while …

In recent years, convolutional neural network (CNN)-based methods have been widely used
for remote sensing (RS) scene classification tasks and have achieved excellent results …

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

With the development of high-resolution satellites, more and more attention has been paid to
remote sensing (RS) scene classification. Convolutional neural networks (CNNs), which …

Remote sensing scene classification remains challenging due to the complexity and variety
of scenes. With the development of attention-based methods, Convolutional Neural …

This letter presents a classifier-constrained deep adversarial domain adaptation (CDADA)
method for cross-domain semisupervised classification in remote sensing (RS) images. A …

In recent years, classification has obtained ever-rising attention and has been applied to
many areas in the field of remote sensing, including land use, forest monitoring, urban …

A new domain adaptation algorithm based on the class centroid and covariance alignment
(CCCA) is proposed for classification of remote sensing images. This approach exploits both …