Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared
to their real counter-parts in speech enhancement, image and signal processing …

Artificial neural networks (ANNs) based machine learning models and especially deep
learning models have been widely applied in computer vision, signal processing, wireless …

Deep learning models have achieved remarkable success in many different fields and
attracted many interests. Several researchers attempted to apply deep learning models to …

Convolutional neural networks (CNNs) have become the state-of-the-art in optical image
processing. Recently, CNNs have been used in polarimetric synthetic aperture radar …

Polarimetric synthetic aperture radar (PolSAR) image classification has been widely applied
in many fields, such as agriculture, meteorology and military. However, some problems …

We present an in-depth statistical comparison among several Complex-Valued Neural
Network (CVNN) models on the Oberpfaffenhofen Polarimetric Synthetic Aperture Radar …

Since conventional Deep Neural Networks (DNNs) use real numbers as their data, they are
unable to capture the imaginary values and the correlations between real and imaginary …

In this paper we provide an exhaustive statistical comparison between Complex-Valued
MultiLayer Perceptron (CV-MLP) and Real-Valued MultiLayer Perceptron (RV-MLP) on …

Despite the state-of-the-art performance of the deep learning methods for Synthetic Aperture
Radar (SAR) data classification, the Real-Valued (RV) networks neglect the phase …

Radar signal and SAR image processing generally require complex-valued representations
and operations, eg, Fourier, wavelet transforms, Wiener, matched filters, etc. However, the …