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

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

doi:10.1109/IGARSS47720.2021.9555149

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

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

Applied Research Center for Environment and Marine Studies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 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 language	English
Title of host publication	IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1311-1314
Number of pages	4
ISBN (Electronic)	9781665403696
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9555149
State	Published - 2021

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2021-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

Access to Document

10.1109/IGARSS47720.2021.9555149

Cite this

Tiwari, S. P., Chaturvedi, S. K., Adhikary, S., Banerjee, S., & Basu, S. (2021). AUTOMATIZED MARINE VESSEL MONITORING FROM SENTINEL-1 DATA USING CONVOLUTION NEURAL NETWORK. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 1311-1314). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS47720.2021.9555149

Tiwari, Surya Prakash ; Chaturvedi, Sudhir Kumar ; Adhikary, Subhrangshu et al. / AUTOMATIZED MARINE VESSEL MONITORING FROM SENTINEL-1 DATA USING CONVOLUTION NEURAL NETWORK. IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1311-1314 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "AUTOMATIZED MARINE VESSEL MONITORING FROM SENTINEL-1 DATA USING CONVOLUTION NEURAL NETWORK",

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.",

keywords = "Artificial intelligence, Convolution neural network, Marine vessel detection, Synthetic aperture radar",

author = "Tiwari, \{Surya Prakash\} and Chaturvedi, \{Sudhir Kumar\} and Subhrangshu Adhikary and Saikat Banerjee and Sourav Basu",

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year = "2021",

doi = "10.1109/IGARSS47720.2021.9555149",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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Tiwari, SP, Chaturvedi, SK, Adhikary, S, Banerjee, S & Basu, S 2021, AUTOMATIZED MARINE VESSEL MONITORING FROM SENTINEL-1 DATA USING CONVOLUTION NEURAL NETWORK. in IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., pp. 1311-1314. https://doi.org/10.1109/IGARSS47720.2021.9555149

AUTOMATIZED MARINE VESSEL MONITORING FROM SENTINEL-1 DATA USING CONVOLUTION NEURAL NETWORK. / Tiwari, Surya Prakash; Chaturvedi, Sudhir Kumar; Adhikary, Subhrangshu et al.
IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1311-1314 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Tiwari, Surya Prakash

AU - Chaturvedi, Sudhir Kumar

AU - Adhikary, Subhrangshu

AU - Banerjee, Saikat

AU - Basu, Sourav

PY - 2021

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N2 - 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.

AB - 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.

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KW - Convolution neural network

KW - Marine vessel detection

KW - Synthetic aperture radar

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M3 - Conference contribution

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Tiwari SP, Chaturvedi SK, Adhikary S, Banerjee S, Basu S. AUTOMATIZED MARINE VESSEL MONITORING FROM SENTINEL-1 DATA USING CONVOLUTION NEURAL NETWORK. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1311-1314. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS47720.2021.9555149

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

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this