Artificial intelligence, and especially deep neural networks, have evolved substantially in the
recent years, infiltrating numerous domains of applications, often greatly impactful to …

In recent years, black-box machine learning approaches have become a dominant modeling
paradigm for knowledge extraction in remote sensing. Despite the potential benefits of …

Water detection from SAR imagery has significant values, such as the flood monitoring and
environmental protection. Nowadays, significant progress has been achieved in water …

Although explainable artificial intelligence (XAI) promises considerable progress in
glassboxing deep learning models, there are challenges in applying XAI to geospatial …

Deep learning-based synthetic aperture radar (SAR) target detection has been developed
for years, with many advanced methods proposed to achieve higher indicators of accuracy …

Abstract Synthetic Aperture Radar (SAR), with its coherent imaging mechanism, has the
unique advantage of all-day and all-weather imaging. As a typical and important topic …

This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …

Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …

Multifeature synthetic aperture radar (SAR) ship classification aims to build models that can
process, correlate, and fuse information from both handcrafted and deep features. Although …

Since significant intraclass differences and inconspicuous interclass variations, fine-grained
aircraft detection in synthetic aperture radar (SAR) images is challenging. Also, the inherent …