Probabilistic water body mapping of GF-3 images based on prior probability estimation

doi:10.11947/j.AGCS.2019.20170732

Abstract

Abstract: We combine k-means cluster algorithm with the statistical model of SAR(synthetic aperture radar) images and develop the probabilistic water body mapping algorithm based on the priori probability estimation. Firstly, we make the statistical model assumption about backscatter values based on Bayesian theory. Then, we classify the images based on cluster algorithm, calculate the prior probability of the water body mapping and estimate the parameters of the statistical model of water distribution.Thewater body probabilistic maps based on GF-3 images in Luquan and Xianning are calculated and then validated with GF-1 images. The algorithm is effective on high-precision probabilistic water body mapping of SAR images.

Key words: probabilistic water body mapping, parameter estimation, synthetic aperture radar (SAR), GF-3

CLC Number:

P237

MENG Lingkui, MAO Xudong, WEI Zushuai, ZHANG Wen. Probabilistic water body mapping of GF-3 images based on prior probability estimation[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(4): 439-447.

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