Paper Info
Title
Region-Based Classification of SAR Images Using Kullback–Leibler Distance Between Generalized Gamma Distributions
Abstract
For the classification of synthetic aperture radar (SAR) images, traditional pixel-based Bayesian classifiers suffer from an intrinsic flaw that categories with serious overlapped probability density functions cannot be well classified. To solve this problem, in this letter, a region-based classifier for SAR images is proposed, where regions, instead of individual pixels, are treated as elements for classification. In the algorithm, each region is assigned to the class that minimizes a criterion referring to the Kullback–Leibler distance. Besides, the generalized gamma distribution , a flexible empirical model, is employed for the statistical modeling of SAR images. Finally, with a synthetic image and an actual SAR image acquired by the EMISAR system, the effectiveness of the proposed algorithm is validated, compared with the pixel-based maximum-likelihood method and two region-based Bayesian classifiers.
Year
DOI
Venue
2015
10.1109/LGRS.2015.2418217
Geoscience and Remote Sensing Letters, IEEE  
Keywords
Field
DocType
classification,kullback–leibler (kl) distance,generalized gamma distribution (gγd),region-based classifier,synthetic aperture radar (sar),remote sensing,data models,synthetic aperture radar,classification algorithms,image segmentation
Synthetic aperture radar,Remote sensing,Image segmentation,Artificial intelligence,Computer vision,Pattern recognition,Statistical model,Pixel,Gamma distribution,Statistical classification,Mathematics,Generalized gamma distribution,Kullback–Leibler divergence
Journal
Volume
Issue
ISSN
PP
99
1545-598X
Citations 
PageRank 
References 
5
0.44
10
Authors
4
Name
Order
Citations
PageRank
Xianxiang Qin150.44
Huanxin Zou218419.43
Shilin Zhou3302.68
Kefeng Ji417617.01