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.
|Title of host publication
|IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2021
|International Geoscience and Remote Sensing Symposium (IGARSS)
Bibliographical notePublisher Copyright:
© 2021 IEEE
- Artificial intelligence
- Convolution neural network
- Marine vessel detection
- Synthetic aperture radar
ASJC Scopus subject areas
- Computer Science Applications
- General Earth and Planetary Sciences