In recent years, unsupervised feature learning based on a neural network architecture has
become a hot new topic for research [1]-[4]. The revival of interest in such deep networks can …

The algorithm of synthetic aperture radar automatic target recognition (SAR-ATR) is
generally composed of the extraction of a set of features that transform the raw input into a …

Tremendous progress has been made in object recognition with deep convolutional neural
networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability …

Artificial intelligence research in the area of computer vision teaches machines to
comprehend and interpret visual data. Machines can properly recognize and classify items …

It is a feasible and promising way to utilize deep neural networks to learn and extract
valuable features from synthetic aperture radar (SAR) images for SAR automatic target …

Ship detection on the SAR images for marine monitoring has a wide usage. SAR technology
helps us to have a better monitoring over intended sections, without considering …

Data-driven classification algorithms have proved to do well for automatic target recognition
(ATR) in synthetic aperture radar (SAR) data. Collecting data sets suitable for these …

The state-of-the-art deep neural networks have made a great breakthrough in remote
sensing image classification. However, the heavy dependence on large-scale data sets …

Deep architectures for classification and representation learning have recently attracted
significant attention within academia and industry, with many impressive results across a …

In an attempt to exploit the automatic feature extraction ability of biologically-inspired deep
learning models, and enhance the learning of target features, we propose a novel deep …