Ship Classification in SAR Images via Densely Connected Triplet CNNs Integrating Fisher Discrimination Regularized Metric Learning
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Student Project | Computer Technology | India | Volume 14 Issue 4, April 2025 | Popularity: 6 / 10


     

Ship Classification in SAR Images via Densely Connected Triplet CNNs Integrating Fisher Discrimination Regularized Metric Learning

Rayna Elsa Mathai, Sindhu Daniel


Abstract: This paper focuses on ship classification from Synthetic Aperture Radar (SAR) images using a Triplet Convolutional Neural Network (CNN) integrated with Fisher Discrimination Regularized Metric Learning. The system involves dataset creation, triplet sampling, model training, and ship type prediction (Cargo, Passenger, Dredging, Tanker). A GUI built with Tkinter allows users to upload SAR images and view the prediction results.


Keywords: Synthetic Aperture Radar, Fisher Discrimination


Edition: Volume 14 Issue 4, April 2025


Pages: 1584 - 1589


DOI: https://www.doi.org/10.21275/SR25416204849


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Rayna Elsa Mathai, Sindhu Daniel, "Ship Classification in SAR Images via Densely Connected Triplet CNNs Integrating Fisher Discrimination Regularized Metric Learning", International Journal of Science and Research (IJSR), Volume 14 Issue 4, April 2025, pp. 1584-1589, https://www.ijsr.net/getabstract.php?paperid=SR25416204849, DOI: https://www.doi.org/10.21275/SR25416204849

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