  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.
