Volume 5 Issue 4
Aug.  2016
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Article Navigation >  Journal of Radars  > 2016  >  5(4): 410-418
Jiang Tie-zhen, Xiao Wen-shu, Li Da-sheng, Liao Tong-qing. Feasibility Study on Passive-radar Detection of Space Targets Using Spaceborne Illuminators of Opportunity[J]. Journal of Radars, 2014, 3(6): 711-719. doi: 10.12000/JR14080
Citation: Du Kangning, Deng Yunkai, Wang Yu, Li Ning. Medium Resolution SAR Image Time-series Built-up Area Extraction Based on Multilayer Neural Network[J]. Journal of Radars, 2016, 5(4): 410-418. doi: 10.12000/JR16060
shu

Medium Resolution SAR Image Time-series Built-up Area Extraction Based on Multilayer Neural Network

DOI: 10.12000/JR16060

  • Institute of Electronics, Chinese Academy of Science, Beijing 100190, China

Funds:

The National Natural Science Foundation of China (61301025), Hundred-Talent Program of the Chinese Academy of Sciences

  • Received Date: 2016-03-19
  • Rev Recd Date: 2016-06-12
  • Publish Date: 2016-08-28
    Abstract
  • To improve the accuracy and stability of built-up area extraction from Synthetic Aperture Radar (SAR) image time series, in this paper, we propose a multilayer neural-network-based built-up area extraction method that combines the characters of time-series images. The proposed method coarsely tags single images and obtains a large number of samples from time-series images that have been processed by a histogram specification procedure. To generate a training sample dataset, we use samples generated from one image to determine network depth and select samples with higher accuracy from the sample set taken from the time-series images. The final model is trained by the selected large and high quality training dataset. We perform two comparison experiments with 38 25-m resolution ENVISAT ASAR images. Using the proposed method, we achieved 90.2% minima accuracy and a 0.725 minima Kappa coefficient, which are much higher than those of the three conventional methods. Thus, the accuracy and stability of built-up area extraction are significantly improved. In addition, the method proposed in this paper has the advantages of requiring minimal manual operation, well generalization, and training efficiency.

     

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