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Deep Multi-Feature Learning for Water Body Extraction from Landsat Imagery

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Abstract

Water body extraction from remote sensing image data has been becoming a really hot topic. Recently, researchers put forward numerous methods for water body extraction, while most of them rely on elaborative feature selection and enough number of training samples. Convolution Neural Network (CNN), one of the implementation models of deep learning, has strong capability for two-dimension images’ classification. A new water body extraction model based on CNNs is established for deep multi-feature learning. Before experiment, image enhancement will be done by Dark Channel Prior. Then we concatenate three kinds of features: spectral information, spatial information that is extracted by Extended Multi-attribute Profile (EMAP) and various water indexes firstly. Next, feature matrixes are acted as the input of CNN-based model for training and classifying. The experimental results showed that the proposed model has better classification performance than Support Vector Machine (SVM) and artificial neural network (ANN). On very limited training set, our model could learn unique and representative features for better water body extraction.

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ACKNOWLEDGMENTS

This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (no. 2016D01C050).

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Authors and Affiliations

  1. Network Center, Xinjiang University, Xinjiang Uygur Autonomous Region, 830008, Urumqi, China

    Long Yu

  2. College of Software, Xinjiang University, Xinjiang Uygur Autonomous Region, 830046, Urumqi, China

    Ruonan Zhang, Shengwei Tian & Liu Yang

  3. College of Information Science and Engineering, Xinjiang University, Xinjiang Uygur Autonomous Region, 830008, Urumqi, China

    Yalong Lv

Authors
  1. Long Yu
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  2. Ruonan Zhang
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  3. Shengwei Tian
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  4. Liu Yang
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  5. Yalong Lv
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Correspondence to Shengwei Tian.

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Yu, L., Zhang, R., Tian, S. et al. Deep Multi-Feature Learning for Water Body Extraction from Landsat Imagery. Aut. Control Comp. Sci. 52, 517–527 (2018). https://doi.org/10.3103/S0146411618060123

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