Convolutional neural network (CNN) has demonstrated impressive ability to represent
hyperspectral images and to achieve promising results in hyperspectral image classification …

The rapid development of remote sensing sensors makes the acquisition, analysis, and
application of hyperspectral images (HSIs) more and more extensive. However, the limited …

Hyperspectral images (HSIs) comprise plenty of information in the spatial and spectral
domain, which is highly beneficial for performing classification tasks in a very accurate way …

This paper proposes a novel contextual method for classification of polarimetric synthetic
aperture radar data. The method combines support vector machine (SVM) and Wishart …

This paper develops several new strategies for remote sensing image classification
postprocessing (CPP) and conducts a systematic study in this area. CPP is defined as a …

Due to that polarimetric synthetic aperture radar (PolSAR) data suffers from missing labeled
samples and complex-valued data, this article presents a novel semi-supervised PolSAR …

This publication presents a computer method allowing river channels to be segmented
based on SAR polarimetric images. Solutions have been proposed which are based on a …

Although deep learning-based methods have been successfully applied to polarimetric
synthetic aperture radar (PolSAR) image classification tasks, most of the available …

For polarimetric synthetic aperture radar (PolSAR) data, various polarimetric signatures can
be obtained by target decomposition techniques, which are of great help for characterizing …

In recent years, sparse representation-based techniques have shown great potential for
pattern recognition problems. In this paper, the problem of polarimetric synthetic aperture …