Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …

Convolutional neural network (CNN) comprises the most common and extensively used
network in the field of deep learning (DL). The design of CNNs was influenced by neurons …

Accurate classification of remote sensing (RS) images is a perennial topic of interest in the
RS community. Recently, transfer learning, especially for fine-tuning pretrained …

Building footprint is a primary dataset of an urban geographic information system (GIS)
database. Therefore, it is essential to establish a robust and automated framework for large …

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 …

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

Scene classification aims to classify various objects and land use classes such as farms,
highways, rivers, and airplanes in the remote sensing images. In recent times, the …

Change detection (CD) is a hot research topic in the remote-sensing (RS) community. With
the increasing availability of high-resolution (HR) RS images, there is a growing demand for …

With the explosive growth of the volume and resolution of high-resolution remote-sensing
(HRRS) images, the management of them becomes a challenging task. The traditional …

The scene classification of remote sensing (RS) images plays an essential role in the RS
community, aiming to assign the semantics to different RS scenes. With the increase of …