Use of a Residual Neural Network to Demonstrate Feasibility of Ship Detection Based on Synthetic Aperture Radar Raw Data

Cascelli, Giorgio; Guaragnella, Cataldo; Nutricato, Raffaele; Tijani, Khalid; Morea, Alberto; Ricciardi, Nicolò; Nitti, Davide Oscar

doi:10.3390/technologies11060178

Open AccessArticle

Use of a Residual Neural Network to Demonstrate Feasibility of Ship Detection Based on Synthetic Aperture Radar Raw Data

by

Giorgio Cascelli

¹,

Cataldo Guaragnella

²

,

Raffaele Nutricato

^1,*,

Khalid Tijani

¹,

Alberto Morea

¹,

Nicolò Ricciardi

¹ and

Davide Oscar Nitti

¹

Geophysical Applications Processing G.A.P. s.r.l., 70126 Bari, Italy

²

Department of Electrics and Information Engineering, Politecnico di Bari, 70126 Bari, Italy

^*

Author to whom correspondence should be addressed.

Technologies 2023, 11(6), 178; https://doi.org/10.3390/technologies11060178

Submission received: 30 August 2023 / Revised: 27 November 2023 / Accepted: 5 December 2023 / Published: 11 December 2023

(This article belongs to the Topic Artificial Intelligence in Sensors, 2nd Volume)

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Abstract

Synthetic Aperture Radar (SAR) is a well-established 2D imaging technique employed as a consolidated practice in several oil spill monitoring services. In this scenario, onboard detection undoubtedly represents an interesting solution to reduce the latency of these services, also enabling transmission to the ground segment of alert signals with a notable reduction in the required downlink bandwidth. However, the reduced computational capabilities available onboard require alternative approaches with respect to the standard processing flows. In this work, we propose a feasibility study of oil spill detection applied directly to raw data, which is a solution not sufficiently addressed in the literature that has the advantage of not requiring the execution of the focusing step. The study is concentrated only on the accuracy of detection, while computational cost analysis is not within the scope of this work. More specifically, we propose a complete framework based on the use of a Residual Neural Network (ResNet), including a simple and automatic simulation method for generating the training data set. The final tests with ERS real data demonstrate the feasibility of the proposed approach showing that the trained ResNet correctly detects ships with a Signal-to-Clutter Ratio (SCR) > 10.3 dB.

Keywords: SAR raw data processing; ship detection; convolutional neural networks

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MDPI and ACS Style

Cascelli, G.; Guaragnella, C.; Nutricato, R.; Tijani, K.; Morea, A.; Ricciardi, N.; Nitti, D.O. Use of a Residual Neural Network to Demonstrate Feasibility of Ship Detection Based on Synthetic Aperture Radar Raw Data. Technologies 2023, 11, 178. https://doi.org/10.3390/technologies11060178

AMA Style

Cascelli G, Guaragnella C, Nutricato R, Tijani K, Morea A, Ricciardi N, Nitti DO. Use of a Residual Neural Network to Demonstrate Feasibility of Ship Detection Based on Synthetic Aperture Radar Raw Data. Technologies. 2023; 11(6):178. https://doi.org/10.3390/technologies11060178

Chicago/Turabian Style

Cascelli, Giorgio, Cataldo Guaragnella, Raffaele Nutricato, Khalid Tijani, Alberto Morea, Nicolò Ricciardi, and Davide Oscar Nitti. 2023. "Use of a Residual Neural Network to Demonstrate Feasibility of Ship Detection Based on Synthetic Aperture Radar Raw Data" Technologies 11, no. 6: 178. https://doi.org/10.3390/technologies11060178

APA Style

Cascelli, G., Guaragnella, C., Nutricato, R., Tijani, K., Morea, A., Ricciardi, N., & Nitti, D. O. (2023). Use of a Residual Neural Network to Demonstrate Feasibility of Ship Detection Based on Synthetic Aperture Radar Raw Data. Technologies, 11(6), 178. https://doi.org/10.3390/technologies11060178

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Use of a Residual Neural Network to Demonstrate Feasibility of Ship Detection Based on Synthetic Aperture Radar Raw Data

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