In recent years, convolutional neural networks (CNNs) have drawn considerable attention
for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR …

This article proposes a multiscale convolutional neural network (CNN) based on component
analysis (CA-MCNN) for synthetic aperture radar (SAR) automatic target recognition (ATR) …

Scholarly work points to 5S+ 1, a simple yet powerful method of initiating quality in
manufacturing, as one of the foundations of Lean manufacturing and the Toyota Production …

A rapid development of marine infrastructures can be observed along the global coasts.
Offshore wind farms, oil and gas platforms, artificial islands, aquaculture, and more, are …

Convolutional neural networks (CNNs) have shown good performance in polarimetric
synthetic aperture radar (PolSAR) image classification. Excellent hand-crafted CNN …

This article aims at performing maritime target classification in SAR images using machine
learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil …

Offshore drilling rigs are the foundation of oil and gas exploitation in water areas. Their
spatial and temporal distribution, state attributes and other information directly reflect the …

The continuous acquisition of spatial distribution information for offshore hydrocarbon
exploitation (OHE) targets is crucial for the research of marine carbon emission activities …

With the dynamic technological development, we are witnessing a major progress in
solutions that allow for the observation of Earth's surface. Small satellites have a significant …

We present in this paper a method to compare scene classification accuracy of C-band
Synthetic aperture radar (SAR) and optical images utilizing both classical and quantum …