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.
@article{arxiv.2304.11717,
title = {Automatized marine vessel monitoring from sentinel-1 data using convolution neural network},
author = {Surya Prakash Tiwari and Sudhir Kumar Chaturvedi and Subhrangshu Adhikary and Saikat Banerjee and Sourav Basu},
journal= {arXiv preprint arXiv:2304.11717},
year = {2023}
}
Comments
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS