受同物异谱和异物同谱现象的影响，对遥感影像进行分类时若仅利用光谱信息则分类精度的提高将会受到限制，而局部空间统计特征可以通过对地物空间聚集度的描述与分析在一定程度上减轻这种影响。本文研究了局部空间统计在不同指数(Moran’s I, Getis-Ord G_i, Geary’s C)、邻域规则和间隔距离下，对高空间分辨率的SPOT 5影像分类精度的影响规律。首先，对波段1进行局部空间统计分析，运算结果作为纹理波段添加到原始的光谱波段中；然后，综合利用光谱波段和纹理波段进行监督分类；最后，选取测试样本进行分类的精度评价，并比较分析不同条件下的分类精度，得到地物分类精度同参数之间的关系与规律。通过分析可以得出Getis-Ord G_i指数对于总体分类精度的提高最理想，总体分类精度从 87.74%提高到95.12%。

Infl uenced by the different spectrum with same object and the same spectrum with different object, when classifying remote sensing images, there will be a limitation on the improvement of the classifi cation accuracy if merely relies on spectral signatures. However, this infl uence could be impaired by the characteristics of local spatial statistics, which are capable of describing and analyzing the spatial autocorrelation. In this paper, the effects which different Local Spatial Statistics indices (Moran’s I, Getis-Ord G_i, Geary’s C), neighborhood rule, and lags have on the classifi cation accuracy of the high resolution remote sensing image: SPOT 5 are discussed. Firstly, band 1 is analyzed regarding to local spatial statistics, and the results are added to original spectral bands as textural ones; Secondly, supervised classifi cation is carried out with both spectral band and textural band; Finally, test samples are selected to assess the classifi cation accuracy and different accuracies on different conditions are juxtaposed with each other to fi gure out the relations and disciplines between the classifi cation accuracy and the parameters. Results indicate that G_i statistics should be the optimum choice to raise the overall classifi cation accuracy, with an increase from 87.74% to 95.12%.