中文摘要：

针对基于像素多特征的高分辨率遥感影像分类算法的＂胡椒盐＂现象和面向对象影像分析方法的＂平滑地物细节＂现象,提出了一种融合像素特征和多尺度区域特征的高分辨率遥感影像分类算法。（1）首先采用均值漂移算法对原始影像进行初始过分割,然后对初始过分割结果进行多尺度的区域合并,形成多尺度分割结果。根据多尺度区域合并RMI指数变化和分割尺度对分类精度的影响,确定最优分割尺度。（2）融合光谱特征、像元形状指数PSI（Pixel Shape Index）、初始尺度和最优尺度区域特征,并对多类型特征进行归一化,最后结合支持向量机（SVM）进行分类。实验结果表明该算法既能有效减少基于像素多特征的高分辨率遥感影像分类算法的＂胡椒盐＂现象,又能保持地物对象的完整性和地物细节信息,提高易混淆类别（如阴影和街道,裸地和草地）的分类精度。

英文摘要：

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 accu- racies 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 over- comes 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 classi- fication methods and the ＂excessive smoothing＂ phenomenon of OBIA, a classification method which fused pixel-based multi- feature 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 classi- fication 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 r