Volume 6 Issue 2
May  2017
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Xu Feng, Wang Haipeng, Jin Yaqiu. Deep Learning as Applied in SAR Target Recognition and Terrain Classification[J]. Journal of Radars, 2017, 6(2): 136-148. doi: 10.12000/JR16130
Citation: Xu Feng, Wang Haipeng, Jin Yaqiu. Deep Learning as Applied in SAR Target Recognition and Terrain Classification[J]. Journal of Radars, 2017, 6(2): 136-148. doi: 10.12000/JR16130

Deep Learning as Applied in SAR Target Recognition and Terrain Classification

doi: 10.12000/JR16130
Funds:  The National Natural Science Foundation of China (61571132, 61571134, 61331020), The Foundation of Shanghai Aerospace Science and Technology
  • Received Date: 2016-11-29
  • Rev Recd Date: 2017-03-14
  • Available Online: 2017-04-24
  • Publish Date: 2017-04-28
  • Deep learning such as deep neural networks has revolutionized the computer vision area. Deep learning-based algorithms have surpassed conventional algorithms in terms of performance by a significant margin. This paper reviews our works in the application of deep convolutional neural networks to target recognition and terrain classification using the SAR image. A convolutional neural network is employed to automatically extract a hierarchic feature representation from the data, based on which the target recognition and terrain classification can be conducted. Experimental results on the MSTAR benchmark dataset reveal that deep convolutional network could achieve a state-of-the-art classification accuracy of 99% for the 10-class task. For a polarimetric SAR image classification, we propose complex-valued convolutional neural networks for complex SAR images. This algorithm achieved a state-of-the-art accuracy of 95% for the 15-class task on the Flevoland benchmark dataset.

     

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