Remote sensing image scene classification with deep learning (DL) is a rapidly growing
field that has gained significant attention in the past few years. While previous review papers …

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 …

An attention mechanism assigns different weights to different features to help a model select
the features most valuable for accurate classification. However, the traditional attention …

Since last decade, deep learning has made exceptional progress in various fields of artificial
intelligence including image and voice recognition, natural language processing. Inspired …

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

With the advances of deep learning, many recent CNN-based methods have yielded
promising results for image classification. In very high-resolution (VHR) remote sensing …

The deep convolutional neural network (DeCNN) is considered one of promising techniques
for classifying the high-spatial-resolution remote sensing (HSRRS) scenes, due to its …

Scene classification has become an active research area in remote sensing (RS) image
interpretation. Recently, Transformer-based methods have shown great potential in …

Significant progress has been achieved in remote sensing image scene classification
(RSISC) with the development of convolutional neural networks (CNNs) and vision …

Metric learning can be very useful to improve the performance of a distance-dependent
classifier. However, separating metric learning from the classifier learning possibly …