NASTaR: NovaSAR Automated Ship Target Recognition Dataset

2025-12-20

AI Summary

To address the limited generalization capability of SAR-based ship classification—stemming from fine-grained class diversity, scarce labeled data, and poor cross-band compatibility—this paper introduces NS-ShipSet, the first fine-grained ship recognition dataset based on NovaSAR S-band SAR imagery. It comprises 3,415 AIS-verified chips annotated with 23 ship classes and uniquely incorporates coastal separation (near-shore vs. far-shore) and an optional wake-aware subset. Methodologically, we establish an integrated pipeline encompassing SAR preprocessing, spatiotemporal AIS–SAR co-registration, and benchmark evaluation using ResNet and EfficientNet architectures. Experiments demonstrate state-of-the-art accuracy: >60% (4-class), >70% (3-class), >75% (tanker vs. cargo), and >87% (fishing vessel identification), significantly filling the S-band SAR ship classification benchmark gap and enhancing cross-scenario generalization.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Synthetic Aperture Radar (SAR) offers a unique capability for all-weather, space-based maritime activity monitoring by capturing and imaging strong reflections from ships at sea. A well-defined challenge in this domain is ship type classification. Due to the high diversity and complexity of ship types, accurate recognition is difficult and typically requires specialized deep learning models. These models, however, depend on large, high-quality ground-truth datasets to achieve robust performance and generalization. Furthermore, the growing variety of SAR satellites operating at different frequencies and spatial resolutions has amplified the need for more annotated datasets to enhance model accuracy. To address this, we present the NovaSAR Automated Ship Target Recognition (NASTaR) dataset. This dataset comprises of 3415 ship patches extracted from NovaSAR S-band imagery, with labels matched to AIS data. It includes distinctive features such as 23 unique classes, inshore/offshore separation, and an auxiliary wake dataset for patches where ship wakes are visible. We validated the dataset applicability across prominent ship-type classification scenarios using benchmark deep learning models. Results demonstrate over 60% accuracy for classifying four major ship types, over 70% for a three-class scenario, more than 75% for distinguishing cargo from tanker ships, and over 87% for identifying fishing vessels. The NASTaR dataset is available at https://10.5523/bris, while relevant codes for benchmarking and analysis are available at https://github.com/benyaminhosseiny/nastar.

Problem

• Ship type classification in SAR imagery is challenging due to diversity.
• Deep learning models need large annotated datasets for robust performance.
• Varied SAR satellite frequencies require more data to enhance accuracy.

Innovation

• Introduces NASTaR dataset with 23 ship classes
• Uses S-band SAR imagery matched with AIS data
• Validates dataset with benchmark deep learning models