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Remote Sensing
Volume 18
Issue 12
10.3390/rs18121998
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Article

Applying MLP and SVM Models to Detect Potential Damages on High-Voltage Power Transmission Towers and Lines Using Multi-Temporal SAR Images

by
Raffaele Nutricato
1,
Alessandro Parisi
1,*,
Alberto Morea
1,
Davide Oscar Nitti
1,
Khalid Tijani
1,
Mirko Di Noia
1,
Filomena Ciola
1,
Enrico Sain
2,
Alberto Bigazzi
3,
Gabriele Mascetti
3,
Gianluca Pari
3,
Maria Virelli
3 and
Cataldo Guaragnella
4
1
Geophysical Applications Processing (GAP) Srl, 70125 Bari, Italy
2
Planetek Italia, 70132 Bari, Italy
3
Italian Space Agency (ASI), 00133 Rome, Italy
4
Department of Electrical and Information Engineering, Polytechnic University of Bari, 70125 Bari, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(12), 1998; https://doi.org/10.3390/rs18121998
Submission received: 3 June 2026 / Accepted: 9 June 2026 / Published: 16 June 2026
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

The essential role of electricity supply for public and private services highlights the need to monitor the stability of power transmission networks during, or immediately after, hazardous events. In the aftermath of calamities, traditional field inspections may be impractical or unsafe, leaving operators without timely information on the condition of critical assets. In this paper, we present and discuss the performance of two automatic Artificial Intelligence (AI)-based models (Multi-Layer Perceptron (MLP) neural network architectures and Support Vector Machine (SVM) model) designed to automatically assess the status of high-voltage transmission towers and power lines through multi-temporal spaceborne Synthetic Aperture Radar (SAR) image analysis. Model development and testing rely on real COSMO-SkyMed Stripmap observations of damaged towers and power lines affected by documented hazardous events across Italy, complemented by simulated tower data generated with a physics-guided, signature-based SAR simulator designed to preserve the observed target-to-background contrast and spatial footprint patterns of real SAR tower signatures. Results indicate that the MLP, trained on either real or simulated data, achieved 100% Overall Accuracy (OA) with no observed false positives or false negatives within the considered visibility-screened real test set, while providing inference times on the order of tenths of milliseconds per target… Computational performance characteristics, operational advantages, and the potential pathway toward satellite on-board porting are discussed to enhance situational awareness and support the prioritisation of interventions during critical events.
Keywords: Synthetic Aperture Radar; COSMO-SkyMed; Multi-Layer Perceptron; Support Vector Machines; remote sensing; high-voltage power transmission network; damage detection; electric infrastructure monitoring Synthetic Aperture Radar; COSMO-SkyMed; Multi-Layer Perceptron; Support Vector Machines; remote sensing; high-voltage power transmission network; damage detection; electric infrastructure monitoring

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MDPI and ACS Style

Nutricato, R.; Parisi, A.; Morea, A.; Nitti, D.O.; Tijani, K.; Di Noia, M.; Ciola, F.; Sain, E.; Bigazzi, A.; Mascetti, G.; et al. Applying MLP and SVM Models to Detect Potential Damages on High-Voltage Power Transmission Towers and Lines Using Multi-Temporal SAR Images. Remote Sens. 2026, 18, 1998. https://doi.org/10.3390/rs18121998

AMA Style

Nutricato R, Parisi A, Morea A, Nitti DO, Tijani K, Di Noia M, Ciola F, Sain E, Bigazzi A, Mascetti G, et al. Applying MLP and SVM Models to Detect Potential Damages on High-Voltage Power Transmission Towers and Lines Using Multi-Temporal SAR Images. Remote Sensing. 2026; 18(12):1998. https://doi.org/10.3390/rs18121998

Chicago/Turabian Style

Nutricato, Raffaele, Alessandro Parisi, Alberto Morea, Davide Oscar Nitti, Khalid Tijani, Mirko Di Noia, Filomena Ciola, Enrico Sain, Alberto Bigazzi, Gabriele Mascetti, and et al. 2026. "Applying MLP and SVM Models to Detect Potential Damages on High-Voltage Power Transmission Towers and Lines Using Multi-Temporal SAR Images" Remote Sensing 18, no. 12: 1998. https://doi.org/10.3390/rs18121998

APA Style

Nutricato, R., Parisi, A., Morea, A., Nitti, D. O., Tijani, K., Di Noia, M., Ciola, F., Sain, E., Bigazzi, A., Mascetti, G., Pari, G., Virelli, M., & Guaragnella, C. (2026). Applying MLP and SVM Models to Detect Potential Damages on High-Voltage Power Transmission Towers and Lines Using Multi-Temporal SAR Images. Remote Sensing, 18(12), 1998. https://doi.org/10.3390/rs18121998

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