Features Extraction and Correlation Analysis of Multi-Source Data for Maritime Targets

LIU Ningbo; ZHANG Zihao; CHEN Baoxin; DONG Yunlong; LI Jia

doi:10.11999/JEIT250200

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LIU Ningbo, ZHANG Zihao, CHEN Baoxin, DONG Yunlong, LI Jia. Features Extraction and Correlation Analysis of Multi-Source Data for Maritime Targets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250200

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LIU Ningbo, ZHANG Zihao, CHEN Baoxin, DONG Yunlong, LI Jia. Features Extraction and Correlation Analysis of Multi-Source Data for Maritime Targets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250200

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Features Extraction and Correlation Analysis of Multi-Source Data for Maritime Targets

doi: 10.11999/JEIT250200 cstr: 32379.14.JEIT250200

1.
Naval Aviation University, Yantai 264001, China
2.
Harbin Engineering University Yantai Research Institute, Yantai 264001, China

Funds: The National Natural Science Foundation of China (62388102, 62101583), The Taishan Scholars Program (tsqn202211246)

Received Date: 2025-03-25
Rev Recd Date: 2025-07-25

Available Online: 2025-07-30

Abstract

Abstract

Objective The growing demand for maritime target detection and recognition has made multi-source information fusion a critical area of research. Different sensing modalities including radar, visible-light imaging, and infrared imaging offer complementary information that can improve detection and classification. However, the correlation among heterogeneous features extracted from these sources remains insufficiently understood. In addition, the effects of observational conditions on feature stability and discriminability needs further investigation. This study investigates the intrinsic relationships among multi-source features, evaluates their robustness under varying conditions, and provides theoretical support for effective multi-source feature fusion in maritime target detection. Methods Nine physically interpretable features are extracted across three categories: radar features (radial dimension, number of peaks, position distribution entropy, cross-range dimension, and relative average amplitude), visible image features (number of edges and horizontal projected width), and infrared image features (high-temperature connected component count and thermal texture energy). To ensure accurate feature extraction, data preprocessing consists of cleaning each compressed dataset. Radar data with excessively low signal-to-noise ratios and images with extensive occlusion are excluded. For dataset construction, radar echo data are visualized frame by frame, and a subset of radar, visible-light, and infrared images is manually annotated using LabelImg. Targets are classified into five types: passenger ships, dry cargo ships, container ships, tugboats, and search-and-rescue vessels. Based on these annotations, deep learning models are independently trained for each modality to automate annotation, and the results are manually validated to ensure quality. A standardized target dataset is then constructed by integrating the manually and automatically labeled data. Radar echo data are temporally aligned with visible-light and infrared images using prior time synchronization information. Features are extracted from each modality, and inter-feature correlations are analyzed. Spearman correlation coefficients are used to quantify relationships, and hypothesis testing is conducted to assess significance, revealing intrinsic associations among multi-source features. Results and Discussions Comparative analysis indicates that the correlation between radar echo and image features is strongly affected by feature attributes. Size-related features show stronger correlations, reflecting greater stability for multi-source data fusion, whereas structural features are more sensitive to observation conditions and exhibit weaker correlations. External factors including target motion state, ship type, and radar polarization mode also influence feature extraction and intermodal correlation. When targets are anchored, posture remains stable and motion blur is reduced, strengthening the correlation between radar structural features and image features. Larger vessels, such as container ships and passenger ships, benefit from multiple scattering centers and larger physical dimensions, which enhance feature extraction and intermodal correlation. In contrast, smaller vessels, such as tugboats and search-and-rescue boats, exhibit irregular structures and weaker radar backscatter, leading to lower correlations. The HH polarization mode, being less sensitive to background clutter, improves radar feature stability across various target types and enhances alignment with image features. Image feature stability also varies by modality: visible-light imaging is effective for extracting structural features, such as contours and edges, but is sensitive to illumination and occlusion; infrared imaging offers more stable size-related features and performs robustly in complex and low-visibility environments. These results highlight the complementary properties of multi-source features and establish a theoretical basis for their integration, supporting improved maritime target detection and classification. Conclusions This study demonstrates the complementary characteristics of multi-source features and their potential to improve maritime target detection and recognition. By analyzing feature correlations and stability across varying observational conditions, the results provide a theoretical foundation for refining multi-source fusion strategies. These insights support the development of more robust and reliable detection algorithms, contributing to enhanced situational awareness in maritime surveillance and defense.
- Marine target feature,
- Multi-source observation,
- Target feature,
- Correlation analysis

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References(21)

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