Deep CNN Supported Recognition of Ship Using SAR Images in Maritime Environment

K. Hemanth Sai, A. B. Bazil Raj
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Abstract

In this paper, we have proposed a method to recognize the ship using Synthetic Aperture Radar(SAR) images. The ability of SAR to form radar images independent of any weather conditions and with large swath width made this monitoring technique very well suited for maritime surveillance. The dataset used for the recognition of ships using SAR images consists of three classes for classification. We have used a Deep Convolution Neural Network(CNN) for the recognition of ships from SAR images. The network is trained rigorously and after testing the sample data with a well-trained network we achieved an accuracy of 90 percent.
海洋环境下基于SAR图像的深度CNN支持船舶识别
本文提出了一种利用合成孔径雷达(SAR)图像识别船舶的方法。SAR能够形成不受任何天气条件影响的雷达图像,并且具有较大的条带宽度,这使得这种监测技术非常适合于海上监视。基于SAR图像的船舶识别数据集分为三类进行分类。我们使用深度卷积神经网络(CNN)从SAR图像中识别船舶。该网络经过严格的训练,在用训练有素的网络测试样本数据后,我们达到了90%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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