ABSTRACT A large number of publications have incorporated deep learning in the process
of remote sensing change detection. In these Deep Learning Change Detection (DLCD) …

In this paper, we propose a remote-sensing scene-classification method based on vision
transformers. These types of networks, which are now recognized as state-of-the-art models …

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 …

Scene classification is an active research topic in the remote sensing community, and
complex spatial layouts with various types of objects bring huge challenges to classification …

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

In remote sensing scene classification (RSSC), features can be extracted with different
spatial frequencies where high-frequency features usually represent detailed information …

The availability of the sheer volume of Copernicus Sentinel-2 imagery has created new
opportunities for exploiting deep learning methods for land use land cover (LULC) image …

Change detection is a process of identifying changed ground objects by comparing image
pairs obtained at different times. Compared with the pixel-level and object-level change …

Remote sensing (RS) scene classification plays an essential role in the RS community and
has attracted increasing attention due to its wide applications. Recently, benefiting from the …

Maize tassels play a critical role in plant growth and yield. Extensive RGB images obtained
using unmanned aerial vehicle (UAV) and the prevalence of deep learning provide a …