Title :
Local histogram based classification of SAR images using spatially dependent mixtures
Author_Institution :
Gebze Inst. of Technol., Gebze, Turkey
Abstract :
We propose a local histogram based mixture model for synthetic aperture radar image classification. In this model, local histograms are modeled by multinomial densities. The model has also a spatially dependent class label prior to achieve a smooth contextual classification. Based on the classification results obtained on real TerraSAR-X image, it is shown that the proposed model is capable of more accurately classifying pixels especially in the heterogeneous regions, like urban areas, compared to conventional mixture model.
Keywords :
image classification; mixture models; radar imaging; synthetic aperture radar; SAR image pixel classification; TerraSAR-X image; local histogram based mixture model; multinomial density; smooth contextual classification; spatially dependent mixture model; synthetic aperture radar; Bayes methods; Computational modeling; Context modeling; Histograms; Niobium; Smoothing methods; Synthetic aperture radar; SAR images; contextual image classification; local histograms; mixture models; multinomial logistic regression;
Conference_Titel :
Image Processing (ICIP), 2014 IEEE International Conference on
Conference_Location :
Paris
DOI :
10.1109/ICIP.2014.7026044