Baoli Liu

An effective unsupervised multiscale segmentation of Synthetic Aperture Radar(SAR)imagery based on Expectation Maximization(EM)and Genetic Algorithm(GA)was proposed.The statistical variations between pixels of scale-to-scale and same scale in SAR imagery were described due to radar speckle for the Mixture Multiscale AutoRegressive(MMAR)model,then the estimation of parameters in MMAR model was given by combining GA with EM algorithm.The number of components of the model was selected by using the Minimum Description Length(MDL)criterion and the segmentation of SAR imagery was implemented.This approach benefits from the properties of GA and the EM algorithm by combining of both into a single procedure.The local optimal solutions were avoided with less sensitivity to its initialization.The experiments on SAR images show that the GA-EM outperforms the EM method.

Initialization

Minimum description length

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Mapping the interactions between rivers and sand dunes: Implications for fluvial and aeolian geomorphology

Geomorphology (2014)

Baoli Liu Tom Coulthard

Bedform

Dominance (genetics)

Sand dune stabilization

10.1016/j.geomorph.2014.12.011

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Citations (136)

Evaluation of turfgrid porous pavements for urban stormwater management in Xiamen Island, Southeast China

Sustainable Water Resources Management (2017)

Baoli Liu Wenzhi Cao Ying Li

First flush

Infiltration (HVAC)

Pervious concrete

10.1007/s40899-017-0147-y

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Citations (2)

Efficient multiscale classification algorithm of SAR image

Jisuanji gongcheng yu sheji (2008)

Baoli Liu

An efficient unsupervised multiscale classification of synthetic aperture radar(SAR) imagery based on the genetic algorithm with expectation maximization(GA-EM) algorithm is proposed.This algorithm is capable of selecting the number of classification of SAR image using the minimum description length(MDL) criterion for Gaussian mixture model.Our approach benefits from the properties of genetic algorithms(GA) and the EM algorithm by combination of both into a single procedure.Therefore,our algorithm enables escaping from local optimal solutions since the algorithm becomes less sensitive to its initialization.Some experiment results on the SAR images are given based on our proposed approach,and compared with EM algorithms.The experiments show that the GA-EM outperforms the other method on segmentation precision.

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Minimum description length

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Geomorphological classification of aeolian-fluvial interactions in the desert region of north China

Journal of Arid Environments (2019)

Xiaomei Li Ping Yan Baoli Liu

Landform

10.1016/j.jaridenv.2019.104021

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Citations (10)