Maritime surveillance remains a critical component of national security and environmental monitoring thereby necessitating the continuous advancement of vessel detection technologies. Traditional methods often struggle with the challenges posed by Synthetic Aperture Radar (SAR) imagery, particularly in detecting small or partially obscured vessels within complex marine environments. This paper introduces a novel approach that significantly enhances the accuracy and efficiency of maritime vessel detection by utilizing advanced deep learning techniques. Utilizing the High-Resolution SAR Images Dataset (HRSID), the proposed method incorporates a sophisticated preprocessing phase that combines Median Filtering for noise reduction and Adaptive Histogram Equalization for contrast enhancement. The novelty in proposed work methodology is a state-of-the-art segmentation process using Mask-RCNN which is well-known for its efficiency in distinguishing objects from cluttered backgrounds, which is quite crucial in marine settings. This is further complemented by the innovative use of DenseNet101 for robust feature extraction, capturing complex vessel characteristics often missed by conventional models. A Convolutional Recurrent Neural Network (CRNN) is then employed for the classification of vessels, integrating spatial and temporal data to enhance detection accuracy. The proposed approach not only fills the existing gap in real-time and reliable small vessel detection but also sets new benchmarks in computational efficiency which is a critical factor for real-time applications. Experimental results demonstrate significant improvements over existing methods in both accuracy and processing speed, promising a substantial impact on the operational capabilities of maritime surveillance systems.
Keywords
Synthetic Aperture Radar (SAR) Imagery; Deep Learning in Maritime Surveillance; Mask-RCNN for Object Segmentation; DenseNet101 Feature Extraction; Convolutional Recurrent Neural Network (CRNN); Adaptive Histogram Equalization.
Z. Yan, X. Song, L. Yang, and Y. Wang, “Ship Classification in Synthetic Aperture Radar Images Based on Multiple Classifiers Ensemble Learning and Automatic Identification System Data Transfer Learning,” Remote Sensing, vol. 14, no. 21, p. 5288, Oct. 2022, doi: 10.3390/rs14215288.
T. Yang, X. Wang, and Z. Liu, “Ship Type Recognition Based on Ship Navigating Trajectory and Convolutional Neural Network,” Journal of Marine Science and Engineering, vol. 10, no. 1, p. 84, Jan. 2022, doi: 10.3390/jmse10010084.
Y. Zhang, L. Guo, Z. Wang, Y. Yu, X. Liu, and F. Xu, “Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion,” Remote Sensing, vol. 12, no. 20, p. 3316, Oct. 2020, doi: 10.3390/rs12203316.
N. S. Gawai, and D. V. Rojatkar, “Literature Review on Ship Detection Methods using Satellite-borne Synthetic Aperture Radar for Maritime Surveillance,” Grenze International Journal of Engineering & Technology (GIJET), 10(1):1-7, 2024.
T. Zhao et al., “Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances,” Remote Sensing, vol. 16, no. 7, p. 1145, Mar. 2024, doi: 10.3390/rs16071145.
X. Chen, Z. Dong, Z. Zhang, C. Tu, T. Yi, and Z. He, “Very High Resolution Synthetic Aperture Radar Systems and Imaging: A Review,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 7104–7123, 2024, doi: 10.1109/jstars.2024.3374429.
D. Princy and V. R. S. Mani, “A Survey of Ship Detection and Classification Techniques,” Soft Computing for Intelligent Systems, pp. 565–602, 2021, doi: 10.1007/978-981-16-1048-6_46.
V. D, V. Indira, S. Umar, B. Pant, M. K. Goyal, and A. B, “Discriminating the Pneumonia-Positive Images from COVID-19-Positive Images Using an Integrated Convolutional Neural Network,” Mathematical Problems in Engineering, vol. 2022, pp. 1–9, Jun. 2022, doi: 10.1155/2022/5643977.
D. Zhao, Z. Zhang, D. Lu, J. Kang, X. Qiu, and Y. Wu, “CVGG-Net: Ship Recognition for SAR Images Based on Complex-Valued Convolutional Neural Network,” IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1–5, 2023, doi: 10.1109/lgrs.2023.3316133.
N. K. Mishra, A. Kumar, and K. Choudhury, “Deep Convolutional Neural Network based Ship Images Classification,” Defence Science Journal, vol. 71, no. 2, pp. 200–208, Mar. 2021, doi: 10.14429/dsj.71.16236.
T. Zhang et al., “HOG-ShipCLSNet: A Novel Deep Learning Network With HOG Feature Fusion for SAR Ship Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–22, 2022, doi: 10.1109/tgrs.2021.3082759.
D. Li, Q. Liang, H. Liu, Q. Liu, H. Liu, and G. Liao, “A Novel Multidimensional Domain Deep Learning Network for SAR Ship Detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–13, 2022, doi: 10.1109/tgrs.2021.3062038.
C. Dechesne, S. Lefèvre, R. Vadaine, G. Hajduch, and R. Fablet, “Multi-task deep learning from Sentinel-1 SAR: ship detection, classification and length estimation,” In BiDS'19: Conference on Big Data from Space, 6(8):1-11, 2019, February.
Y. Guan et al., “Fishing Vessel Classification in SAR Images Using a Novel Deep Learning Model,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–21, 2023, doi: 10.1109/tgrs.2023.3312766.
N. Kathiresan and J. Samuel Manoharan, “A Comparative Analysis of Fusion Techniques Based on Multi Resolution Transforms,” National Academy Science Letters, vol. 38, no. 1, pp. 61–65, Dec. 2014, doi: 10.1007/s40009-014-0300-1.
X. Xu, X. Zhang, and T. Zhang, “Multi-Scale SAR Ship Classification with Convolutional Neural Network,” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 4284–4287, Jul. 2021, doi: 10.1109/igarss47720.2021.9553116.
T. Zhang and X. Zhang, “High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network,” Remote Sensing, vol. 11, no. 10, p. 1206, May 2019, doi: 10.3390/rs11101206.
S. Mukherjee, “Detecting Ships from Satellite Images using Deep Learning Technique,” (Doctoral dissertation, Dublin, National College of Ireland).11(24):2997-2309, 2022.
Y. Tajima, L. Wu, and K. Watanabe, “Development of a Shoreline Detection Method Using an Artificial Neural Network Based on Satellite SAR Imagery,” Remote Sensing, vol. 13, no. 12, p. 2254, Jun. 2021, doi: 10.3390/rs13122254.
S. I. T. Joseph, J. Sasikala, and D. Sujitha Juliet, “RETRACTED ARTICLE: A novel vessel detection and classification algorithm using a deep learning neural network model with morphological processing (M-DLNN),” Soft Computing, vol. 23, no. 8, pp. 2693–2700, Nov. 2018, doi: 10.1007/s00500-018-3645-4.
F. Sharifzadeh, G. Akbarizadeh, and Y. Seifi Kavian, “Ship Classification in SAR Images Using a New Hybrid CNN–MLP Classifier,” Journal of the Indian Society of Remote Sensing, vol. 47, no. 4, pp. 551–562, Oct. 2018, doi: 10.1007/s12524-018-0891-y.
M. M. Stofa, M. A. Zulkifley, and S. Z. Muhammad Zaki, “A deep learning approach to ship detection using satellite imagery,” IOP Conference Series: Earth and Environmental Science, vol. 540, no. 1, p. 012049, Jul. 2020, doi: 10.1088/1755-1315/540/1/012049.
P. Kantamaneni, D. Vetrithangam, M. M. Saisree, S. Shargunam, S. S. Kumar and A. Bekkanti, “Optimized fuzzy c-means (fcm) clustering for high-precision brain image segmentation and diagnosis using densenet features,” Journal of Theoretical and Applied Information Technology, Vol.101, No.24, 2023.
H. Zhu, N. Lin, and D. Leung, “Ship Classification from SAR Images based on Sequence Input of Deep Neural Network,” Journal of Physics: Conference Series, vol. 1549, no. 5, p. 052042, Jun. 2020, doi: 10.1088/1742-6596/1549/5/052042.
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Devika Priyadharshini S
Department of Computer and Information Science, Annamalai University, Annamalai Nagar, Tamil Nadu, India.
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Devika Priyadharshini S and Vadivazhagan K, “Advanced Vessel Detection and Classification in SAR Imagery through Integrated Deep Learning Framework Utilizing Multi-Architecture Neural Synthesis”, Journal of Machine and Computing. doi: 10.53759/7669/jmc202505014.
