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This study proposes an approach which simultaneously uses spatial information and polarimetric data from a RADARSAT-2 quad-polarization satellite image for forest tree species classification. The study area is near the Gounamitz River located in northwestern New Brunswick (Canada). After geometric correction of the image, two statistical models were used for the classification: (1) a Markov random fields model based on an initial segmentation provided by the K-means algorithm to account for the spatial statistical dependencies between adjacent sites; and (2) a K-distribution model with, as parameters, the covariance matrix containing all of the polarimetric information. The classification was optimized using the stochastic simulated annealing algorithm. Validation of the results was performed by comparison with field inventory measurements. Variation of the backscattering coefficient c° obtained for the RADARSAT-2 quad-polarization SAR image with incidence angles of 26 0 and 45 ° ranged from 1 and 3 dB for the different tree species. The results of average and overall accuracies of the classification were respectively 77.13% and 72.35% for the 26° incidence angle image compared to 81.47% and 79.12% for the 45°incidence angle.