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Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units

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Abstract

Image segmentation is an important application of polarimetric synthetic aperture radar. This study aimed to create an 11-layer deep convolutional neural network for this task. The Pauli decomposition formed the RGB image and was used as the input. We created an 11-layer convolutional neural network (CNN). L-band data over the San Francisco bay area and C-band data over Flevoland area were employed as the dataset. For the San Francisco bay PSAR image, our method achieved an overall accuracy of 97.32%, which was at least 2% superior to four state-of-the-art approaches. We provided the confusion matrix over test area, and the kernel visualization. We compared the max pooling and average pooling. We validated by experiment that four convolution layers perform the best. Besides, our method gave better results than AlexNet. The GPU yields a 173× acceleration on the training samples, and a 181× acceleration on the test samples, compared to standard CPU. For the Flevoland PSAR image, our 11-layer CNN also gives better overall accuracy than five state-of-the-art approaches. The convolutional neural network is better than traditional classifiers and is effective in remote sensing image segmentation.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983), and Project of Science and Technology of Henan Province (172102210272).

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Authors and Affiliations

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, Henan, People’s Republic of China

    Shui-Hua Wang, Junding Sun & Yu-Dong Zhang

  2. Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Shui-Hua Wang & Yu-Dong Zhang

  3. West Virginia School of Osteopathic Medicine, 400 N Lee St, Lewisburg, WV, 24901, USA

    Preetha Phillips

  4. National Clinical Research Center for Geriatric Disorders & Xiangya Hospital, Central South University, Changsha, 410083, Hunan, People’s Republic of China

    Guihu Zhao

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  1. Shui-Hua Wang
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Correspondence to Yu-Dong Zhang.

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Wang, SH., Sun, J., Phillips, P. et al. Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units. J Real-Time Image Proc 15, 631–642 (2018). https://doi.org/10.1007/s11554-017-0717-0

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