One-Shot Learning-Based SAR Ship Classification Using New Hybrid Siamese Network
Anil Raj J, Sumam Mary Idicula, Binu Paul
Abstract
Synthetic aperture radar (SAR) ship classification research mainly focuses on large ships with distinctive features. Also, ship types are expanding nowadays. Traditional standard image classification techniques are not adequate for the effective classification of SAR ships due to their fewer available data in some classes (unbalanced data). We are introducing a one-shot learning-based deep learning model built from scratch to classify 16 ship classes to solve these. It generates embeddings of size 20 and uses distance measure for classification. Also, we propose a preprocessing stage (PP-stage) and feature fusion (FF-stage) technique to improve classification accuracy. Experimental results on the OpenSARShip dataset for ship classification reveal that the accuracy is superior to the other state-of-the-art SAR image classifiers.