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

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