CNN-based ship classification method incorporating SAR geometry information

Shreya Sharma; Kenta Senzaki; Yuzo Senda; Hirofumi Aoki

doi:10.1117/12.2325282

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9 October 2018 CNN-based ship classification method incorporating SAR geometry information

Shreya Sharma, Kenta Senzaki, Yuzo Senda, Hirofumi Aoki

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Proceedings Volume 10789, Image and Signal Processing for Remote Sensing XXIV; 107890C (2018) https://doi.org/10.1117/12.2325282
Event: SPIE Remote Sensing, 2018, Berlin, Germany

Abstract

This paper proposes a ship classification method for synthetic aperture radar (SAR) images, which incorporates SAR geometry information into a convolutional neural network (CNN). Most of the conventional methods for ship classification employ appearance-based features. These features extracted from SAR image are not robust to a geometry change because the geometry difference significantly changes the appearance of target objects in SAR images. CNN has a potential to handle the variations in appearance. However, it requires huge training data, which is rarely available in SAR, to implicitly learn geometry-invariant features. In this paper, we propose a CNN-based ship classification method incorporating SAR geometry information. We focus on the incident angle information that is included in a metadata because incident angle change directly affects the appearance of objects. The proposed method enables a network to learn a relationship between the appearance and SAR geometry by utilizing the incident angle information as a condition. Experimental results show that the proposed method improves the classification accuracy by 1.1% as compared to the conventional CNN, which does not utilize incident angle information. Furthermore, our method requires 25% less training data as compared to the conventional CNN to achieve 70% classification accuracy.

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Shreya Sharma, Kenta Senzaki, Yuzo Senda, and Hirofumi Aoki "CNN-based ship classification method incorporating SAR geometry information", Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 107890C (9 October 2018); https://doi.org/10.1117/12.2325282

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