Automatized marine vessel monitoring from sentinel-1 data using convolution neural network
This addresses the problem of automating marine surveillance for coastal security, though it is incremental as it builds on existing CNN methods for a specific domain.
The paper tackled vessel monitoring from Sentinel-1 SAR data by introducing a wavelet transformation-based CNN approach, achieving 95.46% detection accuracy.
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.