A Comparison of Texture and Amplitude based Unsupervised SAR Image Classifications for Urban Area Extraction

Koray Kayabol (1) , Josiane Zerubia (1)

1 AYIN - Models of spatio-temporal structure for high-resolution image processing

We compare the performance of the texture and the amplitude based mixture density models for urban area extraction from high resolution Synthetic Aperture Radar (SAR) images. We use an Auto-Regressive (AR) model with t-distribution error for the textures and a Nakagami density for the amplitudes. We exploit a Multinomial Logistic (MnL) latent class label model as a mixture density to obtain spatially smooth class segments. We combine the Classification EM (CEM) algorithm with the hierarchical agglomeration strategy and a model order selection criterion called Integrated Completed Likelihood (ICL). We test our algorithm on TerraSAR-X data provided by DLR/DFD.

Mots clés: High resolution SAR; TerraSAR-X; classification; texture : multinomial logistic; unsupervised learning; Classification EM

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Machine Learning [stat.ML]; Traitement des images [eess.IV]

Koray Kayabol, Josiane Zerubia. A Comparison of Texture and Amplitude based Unsupervised SAR Image Classifications for Urban Area Extraction. IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012, Jul 2012, Munich, Germany. pp.4054-4057. ⟨hal-00697172⟩