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

Autor:	Tiwari, Surya Prakash, Chaturvedi, Sudhir Kumar, Adhikary, Subhrangshu, Banerjee, Saikat, Basu, Sourav
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Zdroj:	2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 1311-1314
Druh dokumentu:	Working Paper
DOI:	10.1109/IGARSS47720.2021.9555149
Popis:	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. Comment: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.11717 Zobrazit plný text záznamu View this record from Arxiv