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

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          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 timeseries 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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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 September 2016
          : 5
          : 4
          : 410-418
          Affiliations
          [1 ] Institute of Electronics, Chinese Academy of Science, Beijing 100190, China
          Article
          cae2564e41b04d7a94960a2719417ee1
          10.12000/JR16060

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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