Translator Disclaimer
28 July 1997 Bayesian classification of multi look polarimetric SAR images with a generalized multiplicative speckle model
Author Affiliations +
Abstract
In this paper, a maximum likelihood (ML) classification algorithm is proposed to classify multi-look polarimetric SAR images. This algorithm considers a generalized multiplicative speckle model in which three texture factors are assumed to separately affect three polarization channels. We derive the ML estimation of the texture parameters for each polarization channel with the complex Wishart distribution of the multi-look speckle covariance matrix, and design the corresponding ML classifier according to the Bayesian criterion.BOth the texture class statistics and the discriminant function are given in simple closed forms. Further, a method for adaptively producing the a priori probabilities is also presented in order to improve the classification accuracy. This method utilizes the contextual information in a forward procedure, and does not need any iteration. With the NASA/JPL L-band 4-look polarimetric SAR data, the effectiveness of the presented classification algorithm is demonstrated, and using of the adaptive a priori probabilities is shown to result in improved classifications.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Guoqing Liu, ShunJi Huang, Andrea Torre, and Franco S. Rubertone "Bayesian classification of multi look polarimetric SAR images with a generalized multiplicative speckle model", Proc. SPIE 3070, Algorithms for Synthetic Aperture Radar Imagery IV, (28 July 1997); https://doi.org/10.1117/12.281578
PROCEEDINGS
8 PAGES


SHARE
Advertisement
Advertisement
Back to Top