Abstract: [Objective] To clarify the effects of object features on object-oriented classification for tree species based on high spatial resolution remote sensing images and spaceborne polarimetric SAR data, and further evaluate the suitability using collaboration of two kinds of data to process classification for tree species, this study was conducted on Jiangle state-owned forest farm in Jiangle county, Sanming city, Fujian Province.[Method] In this study, QuickBird remote sensing image and Radarsat-2 data were used as the experimental data to carry out object oriented classification for Chinese fir, pine and broad leaved forest predominated in the study area. Three segmentation schemes(based on multispectral band of QuickBird remote sensing image segmentation, Radarsat-2 data segmentation, collaboration of QuickBird remote sensing image and Radarsat-2 segmentation)were adopted when processing object oriented classification and each segmentation scheme was divided to 10 scales(25-250, step size 25). For each scales of three segmentation schemes, 32 kinds of classification features from four aspects of spectral features, topographic features, height and strength features extracted from QuickBird remote sensing image and Radarsat-2 data was used. According to 3 segmentation schemes, 10 classification scales and 4 aspects of objects' features, 120 kinds of classification results for tree species were obtained by using the support vector machine classifier. Later the accuracy of each classification result was evaluated by 4 different evaluation indexes, namely, producer accuracy calculated by confusion matrix, user accuracy, classification accuracy and Kappa coefficient.[Result] The results showed that:the classification accuracy(Kappa coefficient)of all classification schemes showed a similar trend, or increased firstly and then decreased with scale increasing, each combination of classification scheme had an optimal scale. The highest Kappa coefficient based on spectral bands of QuickBird segmentation-classification scheme was 0.84 at an optimal scale of 150; the highest Kappa coefficient based on Radarsat-2 data segmentation-classification scheme was 0.68 at an optimal scale of 125; the highest Kappa coefficient of cooperative segmentation of QuickBird & Radarsat-2 classification scheme was 0.86 at a scale of 100. In all classification schemes, the classification accuracy that using spectral features individually was the lowest especially on small scales; which lowered than those using two collaboration features of spectral + topography, using three collaboration features of spectral + topography + height, using four collaboration features of spectral + topography + height + strength, respectively. The latter two classification schemes had no significant difference. It indicated that introducing more objected features than one spectral feature could improve classification efficiency. Based on cooperative segmentation of QuickBird & Radarsat-2 at its optimal scale of 100, object-oriented classification for tree species obtained the highest classification accuracy(OA=86%, Kappa=0.86)when simultaneously introducing spectral, topographic, height and strength features.[Conclusion] Classification for tree species by collaboration of high spatial resolution remote sensing images and spaceborne polarimetric SAR data combining with optimal scale and multi-objected features had manifest advantages. Combing height information with spectral and topographic features could further increase classification accuracy. This study could improve the efficiency of features selection and accuracy of object-oriented classification. Moreover, it could provide good reference basis for other images based on object-oriented classification technology.

Key words: object-based, object features, SAR, QuickBird, SVM