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

arXiv: https://arxiv.org/abs/2304.11717 · HTML: https://mlsanity.com/paper/2304.11717/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 10 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Built on by (most cited first, 1 of 1)

- [HARBOR: Heading Analysis and Reconstruction from Behavioral Observation and Radar](https://mlsanity.com/paper/2606.14006/) (2026)

## Abstract (arXiv)

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.