Deep Multi-Feature Learning for Water Body Extraction from Landsat Imagery
- Authors: Yu L.1, Zhang R.2, Tian S.2, Yang L.2, Lv Y.3
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Affiliations:
- Network Center, Xinjiang University, Xinjiang Uygur Autonomous Region
- College of Software, Xinjiang University, Xinjiang Uygur Autonomous Region
- College of Information Science and Engineering, Xinjiang University, Xinjiang Uygur Autonomous Region
- Issue: Vol 52, No 6 (2018)
- Pages: 517-527
- Section: Article
- URL: https://journals.rcsi.science/0146-4116/article/view/175573
- DOI: https://doi.org/10.3103/S0146411618060123
- ID: 175573
Cite item
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.
About the authors
Long Yu
Network Center, Xinjiang University, Xinjiang Uygur Autonomous Region
Email: tianshengwei@163.com
China, Urumqi, 830008
Ruonan Zhang
College of Software, Xinjiang University, Xinjiang Uygur Autonomous Region
Email: tianshengwei@163.com
China, Urumqi, 830046
Shengwei Tian
College of Software, Xinjiang University, Xinjiang Uygur Autonomous Region
Author for correspondence.
Email: tianshengwei@163.com
China, Urumqi, 830046
Liu Yang
College of Software, Xinjiang University, Xinjiang Uygur Autonomous Region
Email: tianshengwei@163.com
China, Urumqi, 830046
Yalong Lv
College of Information Science and Engineering, Xinjiang University, Xinjiang Uygur Autonomous Region
Email: tianshengwei@163.com
China, Urumqi, 830008