Abstract: [Objective] The effects of different segmentation scales on the object-oriented tree species classification based on high spatial resolution remote sensing image and spaceborne polarimetric SAR data collaborated were studied, and the suitability of tree species classification based on the two kinds of data collaborated was also evaluated in this research.[Method] QuickBird remote sensing image and Radarsat-2 data are used as the experimental data. 3 segmentation schemes, using QuickBird remote sensing image only, using Radarsat-2 data only, and using QuickBird and Radarsat-2 together,are applied in the object-oriented classification. In every segmentation scheme, 10 segmentation scales (25-250, step 25) are adopted, and the modified Euclidean distance 3 (ED3_modified) is used to to evaluate the segmentation quality. In the 3 segmentation schemes, the respective characteristics and the common characteristics are applied separately in support vector machine classifier to carry on object-oriented tree species classification.[Result] On the 10 segmentation scales, the values of ED3_modified of segmentation with QuickBird and Radarsat-2 collaborated and QuickBird only are significantly lower than those with Radarsat-2 only. The best segmentation (ED3_modified=0.34) is gotten at scale 100 with QuickBird and Radarsat-2 collaborated. On the 10 segmentation scales, the OA of 3 segmentation-classification schemes are low at the small scales. The OA improves as the scale becomes bigger, and reaches the maximum at a scale. Then the OA reduces with the scale increasing. The segmentation-classification using QuickBird and Radarsat-2 together gets the best accuracy at scale 100 (OA=85.55%; Kappa=0.86), and the scheme using QuickBird remote sensing image alone gets the best accuracy at 150(OA=81.11%; Kappa=0.82), the scheme using Radarsat-2 data alone gets the best accuracy at 125(OA=66.67%; Kappa=0.68), OA and ED3_modified are highly correlated(R²=0.73).[Conclusion] At all scales(25-250), the segmentation quality and accuracy of using QuickBird and Radardat-2 together are better than any other segmentation result and accuracies of using only one source of data, and has obvious advantages compared to only use Radarsat-2 data. Segmentation scale plays an important role in tree species classification. Good matching segmentation and reference objects can get higher classification accuracies. At the same time, the classification results are not obviously influenced by slightly over segmentation or insufficient segmentation.

Key words: image segmentation, scale parameter, SAR, QuickBird, Radarsat, SVM