SAR Images Statistical Modeling and Classification Based on the Mixture of Alpha-Stable Distributions
AbstractThis paper proposes the mixture of Alpha-stable (MAS) distributions for modeling statistical property of Synthetic Aperture Radar (SAR) images in a supervised Markovian classification algorithm. Our work is motivated by the fact that natural scenes consist of various reflectors with different types that are typically concentrated within a small area, and SAR images generally exhibit sharp peaks, heavy tails, and even multimodal statistical property, especially at high resolution. Unimodal distributions do not fit such statistical property well, and thus a multimodal approach is necessary. Driven by the multimodality and impulsiveness of high resolution SAR images histogram, we utilize the mixture of Alpha-stable distributions to describe such characteristics. A pseudo-simulated annealing (PSA) estimator based on Markov chain Monte Carlo (MCMC) is present to efficiently estimate model parameters of the mixture of Alpha-stable distributions. To validate the proposed PSA estimator, we apply it to simulated data and compare its performance to that of a state-of-the-art estimator. Finally, we exploit the MAS distributions and a Markovian context for SAR images classification. The effectiveness of the proposed classifier is demonstrated by experiments on TerraSAR-X images, which verifies the validity of the MAS distributions for modeling and classification of SAR images. View Full-Text
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Peng, Y.; Chen, J.; Xu, X.; Pu, F. SAR Images Statistical Modeling and Classification Based on the Mixture of Alpha-Stable Distributions. Remote Sens. 2013, 5, 2145-2163.
Peng Y, Chen J, Xu X, Pu F. SAR Images Statistical Modeling and Classification Based on the Mixture of Alpha-Stable Distributions. Remote Sensing. 2013; 5(5):2145-2163.Chicago/Turabian Style
Peng, Yijin; Chen, Jiayu; Xu, Xin; Pu, Fangling. 2013. "SAR Images Statistical Modeling and Classification Based on the Mixture of Alpha-Stable Distributions." Remote Sens. 5, no. 5: 2145-2163.