With the improvement of spatial resolution of remote sensing image

the details

geometrical structure and texture features of ground objects have been better presented. As the same object type has different spectra or different object types have same spectrum

the statistical separability of different land cover classes in spectral domain is reduced

which is a great challenge to the traditional classification methods based on pixel-features for high spatial resolution remote sensing image. Classification accuracies based on pixel classification methods are improved by fusing pixel texture

structure and shape features. But the pixel-based multi-feature classification methods generally have the shortcomings of"salt and pepper"effect and computational complexity. In recent years

the Object Based Image Analysis（ OBIA） method has been widely concerned. The basic characteristic of OBIA is homogeneous regions as processing units. OBIA method can solve"salt and pepper"problem within traditional methods

and overcomes the shortcomings among pixel-based classification methods. However

a large segmentation scale in OBIA leads to lose detail and present"excessive smoothing"phenomenon. In view of the"salt and pepper"phenomenon of pixel-based multi-feature classification methods and the "excessive smoothing "phenomenon of OBIA

a classification method which fused pixel-based multifeature and multi-scale region-based features is proposed in this paper.（ 1） The over-segment image objects are obtained by mean shift algorithm. Then regions are merged based on the original over-segmentation results through multi-scale

and the multi-scale segmentation results are obtained. According to change of multi-scale regions merged index-RMI and the correlation between classification accuracy and segmentation scale

when the RMI change is small

the adjacent regions are merged

and the RMI change is significant

best segmentation results are obtained in the optimal scale and the adjacent regions merging processes are stopped. The correlation among segmentation scales

segmentation numbers and OA is analyzed. Finally

the optimal segmentation scale is determined.（ 2） Spectral features

shape features and multi-scale region features are extracted

then spectral features

Pixel Shape Index（ PSI） features and region features of the original scale and the optimal scale are fused

and features of various types are normalized. Finally

the classification is implemented by Support Vector Machine（ SVM）. In order to test the effect of the proposed method discussed in this paper

two high spatial resolution hyper spectral remote sensing images are adopted. Some A series of experiment schemes are designed

which include classification methods using pixel-based LBP

GLCM and PSI features

Object-Based Image Analysis（ OBIA）

and single scale segmentation results by e Cognition algorithms and Meanshift（ MS）. Classification results are evaluated by quantitative and qualitative methods

which. are confusion matrix

Overall Accuracy（ OA） and Kappa coefficient as quantitative evaluation and visual discrimination as qualitative evaluation. The classification accuracy of the proposed method is higher than pixel-based multi-feature methods

OBIA method in e Cogniton software and single scale classification results based on MS segmentation method. The experiment results show that the proposed method can effectively take advantages and reduce disadvantages of pixel-based and region-based classification methods and improve classification accuracies of different land cover classes.