Distance metric learning for ship classification in SAR images

Abstract

Synthetic aperture radar (SAR) ship image classification is of great significance in the field of marine ship monitoring. Extracting effective feature representation and constructing suitable classifier can fundamentally improve the accuracy of ship classification. At present, using distance metric learning (DML) algorithm to learn effective distance metrics for classifiers has been widely used in information retrieval and face recognition, but its ability to implement SAR ship image classification is still unknown. In this paper, we show the performance of 4 feature representations and 20 DML algorithms in SAR ship classification. Experimental results show that extracting effective feature representation is essential, and the DML algorithm has the ability to learn better distance metrics.