Distance metric learning for ship classification in SAR images

Yongjie Xu; Haitao Lang; Xiaopeng Chai; Li Ma

doi:10.1117/12.2324954

9 October 2018 Distance metric learning for ship classification in SAR images

Yongjie Xu, Haitao Lang, Xiaopeng Chai, Li Ma

Proceedings Volume 10789, Image and Signal Processing for Remote Sensing XXIV; 107891C (2018) https://doi.org/10.1117/12.2324954
Event: SPIE Remote Sensing, 2018, Berlin, Germany

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.

Citation Download Citation

Yongjie Xu, Haitao Lang, Xiaopeng Chai, and Li Ma "Distance metric learning for ship classification in SAR images", Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 107891C (9 October 2018); https://doi.org/10.1117/12.2324954

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