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Mutong Li

Publications and source records attributed to Mutong Li.

2 recordsLinked to original sources

Complex Isotropic α-Stable-Rician Model for Heterogeneous SAR Images

This article introduces a novel probability distribution model, namely Complex Isotropic α-Stable-Rician (CIαSR), for characterizing the data histogram of synthetic aperture radar (SAR) images. Having its foundation situated on the Lévy α-stable distribution suggested by a generalized Central Limit Theorem, the model promises great potential in accurately capturing SAR image features of extreme heterogeneity. A novel parameter estimation method based on the generalization of method of moments to expectations of Bessel functions is devised to resolve the model in a relatively compact and computationally efficient manner. Experimental results based on both synthetic and empirical SAR data exhibit the CIαSR model's superior capacity in modelling scenes of a wide range of heterogeneity when compared to other state-of-the-art models as quantified by various performance metrics. Additional experiments are conducted utilizing large-swath SAR images which encompass mixtures of several scenes to help interpret the CIαSR model parameters, and to demonstrate the model's potential application in classification and target detection.

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Fast Cauchy-Rician Modelling of SAR Images with Method of Algebraic Moments Estimator

SAR technology has been intensively implemented for geo-sensing and mapping purposes due to its advantages of high azimuthal resolution and weather-independent operation compared to other remote sensing technologies. Modelling SAR image data consequently becomes a prominent topic of interest, especially for data populations with impulsive signal features, which are common in SAR images of urban areas. A recently proposed model named Cauchy-Rician has manifested great potential in modelling extremely heterogeneous SAR images, yet the work only provided a MCMC-based parameter estimator that demands considerable computational power. In this work, a novel analytical parameter estimation method based on algebraic moments is proposed to provide stable and accurate estimation of the parameters of the Cauchy-Rician model with significant improvement on computation speed.

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