Fully Convolution Neural Networks for Water Extraction of Remote Sensing Images

doi:10.13474/j.cnki.11-2246.2018.0173

Abstract

Abstract: This paper presented fully convolution neural networks for extracting water from remote sensing images.The paper introduced the basic principle and method of three kinds of models of fully convolution neural networks.For extracting the water target,firstly,the image data were gathered and labeled two classes——water and background.By inference and learning in fully convolution neural networks,the trained models were obtained.Finally,water target of the test images was extracted.The feasibility of the proposed models was validated by comparing the result of extraction with that of the traditional thresholding method based on image spectral features and GrabCut algorithm based on graph theory.

Key words: remote sensing images, water extraction, fully convolution neural networks, thresholding method, GrabCut

CLC Number:

P237

WANG Xue, SUI Lichun, ZHONG Mianqing, LI Dingmeng, DANG Lili. Fully Convolution Neural Networks for Water Extraction of Remote Sensing Images[J]. 测绘通报, 2018, 0(6): 41-45.

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