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arXiv · 2304.11717

Automatized marine vessel monitoring from sentinel-1 data using convolution neural network

Surya Prakash Tiwari·Sudhir Kumar Chaturvedi·Subhrangshu Adhikary·Saikat Banerjee·Sourav Basu
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Abstract

The advancement of multi-channel synthetic aperture radar (SAR) system is considered as an upgraded technology for surveillance activities. SAR sensors onboard provide data for coastal ocean surveillance and a view of the oceanic surface features. Vessel monitoring has earlier been performed using Constant False Alarm Rate (CFAR) algorithm which is not a smart technique as it lacks decision-making capabilities, therefore we introduce wavelet transformation-based Convolution Neural Network approach to recognize objects from SAR images during the heavy naval traffic, which corresponds to the numerous object detection. The utilized information comprises Sentinel-1 SAR-C dual-polarization data acquisitions over the western coastal zones of India and with help of the proposed technique we have obtained 95.46% detection accuracy. Utilizing this model can automatize the monitoring of naval objects and recognition of foreign maritime intruders.

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Surya Prakash Tiwari, Sudhir Kumar Chaturvedi, Subhrangshu Adhikary, Saikat Banerjee, Sourav Basu. 2023-04-23. Automatized marine vessel monitoring from sentinel-1 data using convolution neural network. https://doi.org/10.1109/igarss47720.2021.9555149

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