Remote sensing scene (RSS) image classification plays a vital role in various fields such as
urban planning and environmental protection. However, due to higher inter-class similarity …

Polarimetric decomposition of oriented buildings is challenging due to their variable
orientation angles and structures. Both vegetated and oriented built-up areas generate the …

A polarimetric synthetic aperture radar (PolSAR) has great potential in ground target
classification. However, current methods experience difficulties in separating forests and …

Extraction of built-up areas from polarimetric synthetic aperture radar (PolSAR) images plays
a crucial role in disaster management. The polarimetric orientation angles (POAs) of built-up …

Ambiguity exists in interpreting polarimetric synthetic aperture radar (PolSAR) L-band
backscatter data from forested and urban environments. Two types of ambiguity are studied …

Influenced by the diversified target structures and complex electromagnetic environments,
accurate maritime ship detection using polarimetric synthetic aperture radar (PolSAR) …

Inspired by the success of Convolutional Neural Network (CNN)-based deep learning
methods for optical image segmentation, there is a growing interest in applying these …

Polarimetric features extracted from polarimetric synthetic aperture radar (PolSAR) images
contain abundant back-scattering information about objects. Utilizing this information for …

In the context of escalating frequency spectrum congestion, the prevalence of radio
frequency interference (RFI) poses a growing challenge for weather radars, compromising …

Although deep learning-based methods have made remarkable achievements in
polarimetric synthetic aperture radar (PolSAR) image classification, these methods require a …