Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 10, Issue 1, Pages 96-100Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2012.2193869
Keywords
Hierarchical Markov random fields (MRFs); supervised classification; synthetic aperture radar (SAR); textural features; urban areas; wavelets
Categories
Funding
- Direction Generale de l'Armement (France)
- Institut National de Recherche en Informatique et Automatique (France)
Ask authors/readers for more resources
This letter addresses the problem of classifying synthetic aperture radar (SAR) images of urban areas by using a supervised Bayesian classification method via a contextual hierarchical approach. We develop a bivariate copula-based statistical model that combines amplitude SAR data and textural information, which is then plugged into a hierarchical Markov random field model. The contribution of this letter is thus the development of a novel hierarchical classification approach that uses a quad-tree model based on wavelet decomposition and an innovative statistical model. The performance of the developed approach is illustrated on a high-resolution satellite SAR image of urban areas.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available