SMART-Ship: A Comprehensive Synchronized Multi-modal Aligned Remote Sensing Targets Dataset and Benchmark for Berthed Ships Analysis
Chen-Chen Fan, Peiyao Guo, Linping Zhang, Kehan Qi, Haolin Huang, Yong-Qiang Mao, Yuxi Suo, Zhizhuo Jiang, Yu Liu, You He

TL;DR
SMART-Ship is a new multi-modal remote sensing dataset with synchronized, annotated images of berthed ships across five modalities, enabling advanced maritime analysis and benchmarking.
Contribution
The paper introduces SMART-Ship, a comprehensive multi-modal dataset with detailed annotations and benchmarks for berthed ships analysis in remote sensing.
Findings
The dataset contains 1092 multi-modal image sets covering 38,838 ships.
Benchmark results demonstrate the dataset's effectiveness for various RS tasks.
Experiments reveal promising directions for multi-modal maritime remote sensing.
Abstract
Given the limitations of satellite orbits and imaging conditions, multi-modal remote sensing (RS) data is crucial in enabling long-term earth observation. However, maritime surveillance remains challenging due to the complexity of multi-scale targets and the dynamic environments. To bridge this critical gap, we propose a Synchronized Multi-modal Aligned Remote sensing Targets dataset for berthed ships analysis (SMART-Ship), containing spatiotemporal registered images with fine-grained annotation for maritime targets from five modalities: visible-light, synthetic aperture radar (SAR), panchromatic, multi-spectral, and near-infrared. Specifically, our dataset consists of 1092 multi-modal image sets, covering 38,838 ships. Each image set is acquired within one week and registered to ensure spatiotemporal consistency. Ship instances in each set are annotated with polygonal location…
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