2023/06/07 by Mutong Li, Li, Mutong, Erçan E. Kuruoğlu +1
Computer Science · Engineering · Mathematics · #Bayesian Methods and Mixture Models #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Remote-Sensing Image Classification #Statistical Methods and Inference #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2306.04383
openalex publication_date 2023/06/07 · openalex created_date 2023/06/09 · openalex updated_date 2026/07/28
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.