Recognizing Snow Cover From Polarimetric SAR Image Based On Markov Random Field Model

Snow cover is an important component of the cryosphere.It plays an important role in global climate change,regulating surface radiation energy budget and balance.As freshwater resource,snow cover is very significant for regional water cycle,climate change,and hydrological investigation research.Recognition of snow provides a basis for snow water equivalence estimates,snowmelt runoff simulation,snow disaster evaluation,and water resources management.Synthetic Aperture Radar(SAR)technology,which has high spatial resolution,can obtain the large range snow cover information in all-weather and all-time,especially for the poor weather conditions and high-altitude cold regions.The polarimetric SAR(PolSAR)can get the scattering information of ground objects with different polarization modes,which provides more information for the snow cover recognition.How to overcome the influence of speckle noise and topography is an urgent problem.This paper focuses on the need of National Natural Science Foundation "Joint inversion of snow water equivalence based on SAR and high-resolution optical remote sensing"(Grant No.41271353).The full polarimetric RADARSAT-2 image in snow accumulation and snow melt period is utilized in this study.The study area is located in Manasi River basin of Tianshan Mountain.First,the microwave characteristics and the image representations of snow are discussed.Then,the snow recognition algorithm based on Markov Random Field(MRF)model is proposed,which uses the spatial-contextual information and prior knowledge.Finally,this method is employed to extract snow cover area from PolSAR images.The main contents and conlusions are as follows:(1)Analysis of the microwave properties and SAR image characterization of snow cover.The microwave characteristics are affected by the physical properties of snow and the parameters of the SAR system.For dry snow cover,the main backscattering is surface scattering from the snow-ground.In wet snow,the absorption loss is high and the dominate backscattering is surface scattering of air-snow.Research shows that the backscattering coefficients and the total scattered power both are lower than other surface features of the study area.The backscattering coefficient of snow accumulation period declined about 10 dB compared with snow melt period.According to the characterization analysis,the backscattering coefficients in cross polarization mode and the total scattered power have a good ability to distinguish the snow and snow-free.The polarimetric scattering matrix contains scattering signal and relationship of four polarization channels,which has more abundant information than single polarization data.The statistical model of multilook complex covariance matrix data can be expressed by complex Wishart distribution or K-Wishart distribution.(2)Construction of the snow cover recognition of polarimetric SAR image besed on Markov Random Field.In order to reduce the effect of spekle noise,a novel recognition model of snow cover is proposed using MRF.MRF can take advantage of spatial contextural information,so that it can reduce the effect of speakle noise.The algorithm uses the K-Wishart distribution for the PolSAR data statistics model,because it has a good fit on the real PolSAR data.In this method,the PolSAR image is initialized by Wishart distance algorithom at first.Then,the model parameter is estimated by iteration and the maximum a posteriori(MAP)is sloved by iterative conditional mode(ICM)algorithom.Until the model is convergenced,the optimal snow recognition results are acquired.(3)Analysis of the recognition result of snow cover.The proposed model based on MRF,is used to recognize snow cover from PolSAR images.Using the measured ground truth data verifies the results of snow cover recognition.What's more,the quantitative indexes,Precision,Recall and F-score,are calculated to evaluate the recognition results.Compared with the other methods,the method based on MRP model combined with a K-Wishart distribution for the PolSAR data statistics model achieves higher accuracy and better results.This recognition model can obtain the homogeneous and reliable snow cover results even on the SAR data with a high degree of speckle.Aiming at the disadvantage of SAR image with speckle noise,this study proposed the model of snow cover recognition based on the MRF model,combining the statistical a priori knowledge with the the spatial contextual information,which can obtain the exact and complete snow cover results.It provides a new idea for snow cover recognition with SAR image,and has a certain theoretical and applied innovation.