Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing
image analysis over the past few years. In this study, the major DL concepts pertinent to …

Recently, convolutional neural networks (CNNs) showed excellent performance in many
tasks, such as computer vision and remote sensing semantic segmentation. Especially, the …

In the field of content-based remote sensing (RS) image retrieval, convolutional neural
networks (CNNs) have been demonstrating overwhelming superiority among other methods …

With the increasing number of remote sensing ship images, it's vitally important to search for
the ship objects that users are interested in from the remote sensing image big data. The …

At present, nonparametric subspace classifiers, such as collaborative representation-based
classification (CRC) and sparse representation-based classification (SRC), are widely used …

Conventional geographic object-based image analysis (GEOBIA) land cover classification
methods by using very high resolution images are hardly applicable due to their complex …

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 …

Nowadays, several remote sensing image capturing technologies are used ranging from
unmanned aerial vehicles to satellites. Powerful learning-based discriminative features play …

Classification is needed in disaster investigation, traffic control, and land-use resource
management. How to quickly and accurately classify such remote sensing imagery has …

In order to effectively extract features and improve classification accuracy for hyperspectral
remote sensing images (HRSIs), the advantages of enhanced particle swarm optimization …