Central to the looming paradigm shift toward data-intensive science, machine-learning
techniques are becoming increasingly important. In particular, deep learning has proven to …

The recent hype about artificial intelligence has sparked renewed interest in applying the
successful deep learning (DL) methods for image recognition, speech recognition, robotics …

Deep learning has become an area of interest to the researchers in the past few years.
Convolutional Neural Network (CNN) is a deep learning approach that is widely used for …

Following the great success of deep convolutional neural networks (CNNs) in computer
vision, this paper proposes a complex-valued CNN (CV-CNN) specifically for synthetic …

Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …

Deep learning in remote sensing has received considerable international hype, but it is
mostly limited to the evaluation of optical data. Although deep learning has been introduced …

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …

Automated water body detection from satellite imagery is a fundamental stage for urban
hydrological studies. In recent years, various deep convolutional neural network (DCNN) …

Satellite imagery offers extensive information that can be used for a variety of societal
applications, from the number of buildings in a metropolis to the land cover types of a …

Polarimetric synthetic aperture radar (PolSAR) image classification is an important
application. Advanced deep learning techniques represented by deep convolutional neural …