AUTOMATIZED MARINE VESSEL MONITORING FROM SENTINEL-1 DATA USING CONVOLUTION NEURAL NETWORK

Surya Prakash Tiwari, Sudhir Kumar Chaturvedi, Subhrangshu Adhikary, Saikat Banerjee, Sourav Basu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1311-1314
Number of pages4
ISBN (Electronic)9781665403696
DOIs
StatePublished - 2021

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2021-July

Bibliographical note

Publisher Copyright:
© 2021 IEEE

Keywords

  • Artificial intelligence
  • Convolution neural network
  • Marine vessel detection
  • Synthetic aperture radar

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences

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