Irregular Tessellation and Statistical Modeling Based Regionalized Segmentation for SAR Intensity Image

Autor:	Hongyun Zhang, Guanghui Wang, Zhao Quanhua, Li Yu
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	Synthetic aperture radar 0209 industrial biotechnology Tessellation (computer graphics) Computer science Science Posterior probability 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Computer Science::Computational Geometry symbols.namesake 020901 industrial engineering & automation Segmentation 021101 geological & geomatics engineering ComputingMethodologies_COMPUTERGRAPHICS bayesian inference irregular tessellation Bayesian inference reversible-jump Markov chain Monte Carlo (RJMCMC) maximum a posteriori (MAP) Statistical model Markov chain Monte Carlo reversible-jump markov chain monte carlo (rjmcmc) Polygon symbols General Earth and Planetary Sciences maximum a posteriori (map) Voronoi diagram Algorithm
Zdroj:	Remote Sensing; Volume 12; Issue 5; Pages: 753 Remote Sensing, Vol 12, Iss 5, p 753 (2020)
ISSN:	2072-4292
DOI:	10.3390/rs12050753
Popis:	This paper presents a regionalized segmentation method for synthetic aperture radar (SAR) intensity images based on tessellation with irregular polygons. In the proposed method, the image domain is partitioned into a collection of irregular polygons, which are constructed using sets of nodes and are used to fit homogeneous regions with arbitrary shapes. Each partitioned polygon is taken as the basic processing unit. Assuming the intensities of the pixels in the polygon follow an independent and identical gamma distribution, the likelihood of the image intensities is modeled. After defining the prior distributions of the tessellation and the parameters for the likelihood model, a posterior probability model can be built based on the Bayes theorem as a segmentation model. To obtain optimal segmentation, a reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm is designed to simulate from the segmentation model, where the move operations include updating the gamma distribution parameter, updating labels, moving nodes, merging polygons, splitting polygons, adding nodes, and deleting nodes. Experiments were carried out on synthetic and real SAR intensity images using the proposed method while the regular and Voronoi tessellation-based methods were also preformed for comparison. Our results show the proposed method overcomes some intrinsic limitations of current segmentation methods and is able to generate good results for homogeneous regions with different shapes.
Databáze:	OpenAIRE
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