DocumentCode :
2208290
Title :
Polsar region classifier based on stochastic distances and hypothesis tests
Author :
Silva, Wagner ; Freitas, Corina ; Sant´Anna, Sidnei ; Frery, Alejandro C.
Author_Institution :
Inst. Nac. de Pesquisas Espaciais, São José dos Campos, Brazil
fYear :
2012
fDate :
22-27 July 2012
Firstpage :
1473
Lastpage :
1476
Abstract :
This work presents a region based classifier for Polarimetric SAR (PolSAR) images. The classifier uses the stochastic distances derived from the complex Wishart Model, obtained from the h-φ family of divergences. Adittionaly, a hypothesis test derived from the stochastic distance is also employed in the classification process. The region based classifier, using the Bhattacharyya distance, was applied to a polarimetric SIR-C image from an agricultural area in northeastern Brazil. The region based classification result significantly overperformed the a pixel based/contextual PolSAR classification based on the Maximum Likelihood/Iterated Conditional Modes. Such evidence lead us to conclude that the region based stochastic distance and hypothesis test classifier offers a good potential at identifying the land cover classes on a PolSAR image.
Keywords :
geophysical image processing; image classification; iterative methods; maximum likelihood estimation; radar computing; radar imaging; radar polarimetry; stochastic processes; Bhattacharyya distance; PoLSAR region classifier; PolSAR imaging; agricultural area; complex Wishart Model; h-φ family of divergence; hypothesis testing; maximum likelihood-iterated conditional mode; northeastern Brazil; pixel based-contextual PolSAR classification; polarimetric SAR imaging; polarimetric SIR-C image; stochastic distance; Covariance matrix; Image segmentation; Maximum likelihood estimation; Remote sensing; Stochastic processes; Synthetic aperture radar; Training; Hypothesis Tests; PolSAR; Region Based Classification; Stochastic Distances;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location :
Munich
ISSN :
2153-6996
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-6996
Type :
conf
DOI :
10.1109/IGARSS.2012.6351256
Filename :
6351256
Link To Document :
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