Volume 6 Issue 5
Oct.  2017
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Article Navigation >  Journal of Radars  > 2017  >  6(5): 514-523
Zhao Juanping, Guo Weiwei, Liu Bin, Cui Shiyong, Zhang Zenghui, Yu Wenxian. Convolutional Neural Network-based SAR Image Classification with Noisy Labels[J]. Journal of Radars, 2017, 6(5): 514-523. doi: 10.12000/JR16140
Citation: Zhao Juanping, Guo Weiwei, Liu Bin, Cui Shiyong, Zhang Zenghui, Yu Wenxian. Convolutional Neural Network-based SAR Image Classification with Noisy Labels[J]. Journal of Radars, 2017, 6(5): 514-523. doi: 10.12000/JR16140
shu

Convolutional Neural Network-based SAR Image Classification with Noisy Labels

DOI: 10.12000/JR16140
  • ①.

    Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai 200240, China

  • ②.

    Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Wessling 82234, Germany

Funds:  The National Natural Science Foundation of China (61331015), The China Postdoctoral Science Foundation (2015M581618)
  • Received Date: 2016-12-06
  • Rev Recd Date: 2017-04-07
    • Available Online: 2017-04-21
  • Publish Date: 2017-10-28
    Abstract
  • SAR image classification is an important task in SAR image interpretation. Supervised learning methods, such as the Convolutional Neural Network (CNN), demand samples that are accurately labeled. However, this presents a major challenge in SAR image labeling. Due to their unique imaging mechanism, SAR images are seriously affected by speckle, geometric distortion, and incomplete structural information. Thus, SAR images have a strong non-intuitive property, which causes difficulties in SAR image labeling, and which results in the weakened learning and generalization performance of many classifiers (including CNN). In this paper, we propose a Probability Transition CNN (PTCNN) for patch-level SAR image classification with noisy labels. Based on the classical CNN, PTCNN builds a bridge between noise-free labels and their noisy versions via a noisy-label transition layer. As such, we derive a new CNN model trained with a noisily labeled training dataset that can potentially revise noisy labels and improve learning capacity with noisily labeled data. We use a 16-class land cover dataset and the MSTAR dataset to demonstrate the effectiveness of our model. Our experimental results show the PTCNN model to be robust with respect to label noise and demonstrate its promising classification performance compared with the classical CNN model. Therefore, the proposed PTCNN model could lower the standards required regarding the quality of image labels and have a variety of practical applications.

     

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