Since its first flight in 2007,the UAVSAR instrument of NASA has acquired a large number of fully Polarimetric SAR(PolSAR)data in very high spatial resolution.It is possible to observe small spatial features in this t...Since its first flight in 2007,the UAVSAR instrument of NASA has acquired a large number of fully Polarimetric SAR(PolSAR)data in very high spatial resolution.It is possible to observe small spatial features in this type of data,offering the opportunity to explore structures in the images.In general,the structured scenes would present multimodal or spiky histograms.The finite mixture model has great advantages in modeling data with irregular histograms.In this paper,a type of important statistics called log-cumulants,which could be used to design parameter estimator or goodness-of-fit tests,are derived for the finite mixture model.They are compared with logcumulants of the texture models.The results are adopted to UAVSAR data analysis to determine which model is better for different land types.展开更多
基金This work has been supported in part by the Shenzhen Science&Technology Program[grant number JSGG20150512145714247]the State Key Program of National Natural Science of China[grant number 61331016]National Key Research Plan of China[grant number 2016YFC0500201-07].
文摘Since its first flight in 2007,the UAVSAR instrument of NASA has acquired a large number of fully Polarimetric SAR(PolSAR)data in very high spatial resolution.It is possible to observe small spatial features in this type of data,offering the opportunity to explore structures in the images.In general,the structured scenes would present multimodal or spiky histograms.The finite mixture model has great advantages in modeling data with irregular histograms.In this paper,a type of important statistics called log-cumulants,which could be used to design parameter estimator or goodness-of-fit tests,are derived for the finite mixture model.They are compared with logcumulants of the texture models.The results are adopted to UAVSAR data analysis to determine which model is better for different land types.
