In recent years, deep learning has evolved as a rapidly growing and stimulating field of
machine learning and has redefined state-of-the-art performances in a variety of …

Removing redundant features and improving classifier performance necessitates the use of
meta-heuristic and deep learning (DL) algorithms in feature selection and classification …

It is necessary to adequately extract scattering characteristics from polarimetric synthetic
aperture radar (PolSAR) data for land-cover classification. Current interpretation …

Synthetic aperture radar (SAR) images can capture abundant spatial and polarimetric
information of land cover objects, and thus polarimetric SAR (PolSAR) image classification …

Polarimetric synthetic aperture radar (PolSAR) images with multiple polarimetric channels
have high discrimination ability. So, they are appropriate data for classification applications …

With the development of deep learning technology, the application of convolutional neural
network (CNN) and vision transformer (ViT) for polarimetric synthetic aperture radar …

Most existing polarimetric synthetic aperture radar (PolSAR) image classification methods
are only valid under the assumption of identical imaging platforms and terrain categories for …

Recently, low THz radar-based measurement and classification for archaeology emerged as
a new imaging modality. In this paper, we investigate the classification of pottery shards, a …

Polarimetric synthetic aperture radar (PolSAR) images contain rich information about back-
scattering and physical characteristics of targets. So, they have high ability for discrimination …

Polarimetric synthetic aperture radar (PolSAR) images are classified mainly according to the
backscattering information of ground objects. For regions with complex backscattering …