Deep Learning for Maritime Target Interpretation in Satellite Synthetic Aperture Radar Imagery

Dear Colleagues,

Satellite-based Synthetic Aperture Radar (SAR) systems have emerged as a cornerstone in maritime monitoring due to their capability for day-and-night, all-weather imaging. With the deployment of new-generation SAR missions—such as Gaofen-3, HaiSi-1, Sentinel-1, RADARSAT Constellation, and SAOCOM—an abundance of medium-resolution (~10 m) SAR data has become available, enabling large-scale maritime surveillance applications.

Despite significant progress, automatic interpretation of maritime SAR imagery remains technically challenging. Core difficulties include multi-scale small target detection, robustness against speckle noise and sea clutter, and high-precision localization under varying environmental conditions. Traditional rule-based and feature-engineered methods have reached their limits in accuracy and generalization, creating a significant need for deep learning-based solutions.

This Special Issue aims to gather innovative research that explores the integration of deep neural networks with SAR data for maritime target interpretation. We welcome the submission of studies focusing on novel detection and segmentation algorithms, domain adaptation, lightweight architectures for real-time deployment, and multimodal data fusion. Particular attention will be given to works that demonstrate robustness across complex ocean scenes and leverage emerging SAR datasets from Chinese and international missions.

Topics of interest include, but are not limited to, the following:

Deep learning for maritime target detection and classification in SAR imagery;

Transformer-based and anchor-free models for complex SAR scenes;

Denoising, speckle suppression, and super-resolution in SAR preprocessing;

Real-time and onboard algorithms for SAR-based maritime monitoring;

Fusion with AIS, optical, and meteorological data;

Benchmark dataset construction and cross-platform generalization.

Dr. Nan Xu
Dr. Jiaqi Yao
Dr. Shijin Li
Guest Editors

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