中文摘要：

为改善SPOT5图像森林分类精度,采用面向对象的图像分析方法,对图像分割、对象特征提取与筛选、多分类器分类与结合进行探索,采用大尺度分割-基于规则的分类-基于分类的分割-分区控制-底层分类-逐层向上合并的技术路线,试验了最小距离、马氏距离、Bayes、模糊分类和支持向量机5个分类器。结果表明：在森林分布破碎、类型和种类多样、结构复杂的南方人工林区,总体分类精度最高的Bayes分类器,对以龄组为基础包含22个类型的第3级分类的总体精度达到了79.38%,以树种为基础包含15个类型的第2级分类的总体精度达到了81.82%,以森林类型为基础包含9个类型的第1级分类的总体精度达到了86.33%。在景观复杂地区的森林分层分类中,由底层分类开始、逐层向上合并的方法,效果比由顶层分类开始、逐层往下分类的方法更好。ETM＋作为辅助数据,较大程度地提高了SPOT5图像的分类精度,但ETM＋图像不能实质性参与分割过程,只能用于提取对象特征,否则会导致对象同质性差、特征变异,降低分类精度。

英文摘要：

Aimed to improve the classification accuracy of SPOT5 imagery,image segmentation,object feature extraction and selection,multi-classifiers and combination had been studied in this paper. A systemic approach to do classification of imagery was present. The proposed method is a five-step object-oriented classification routine that involves the integration of： imagery segmentation with larger scale, rule-based classification, classification-based segmenting, hierarchical classification under sub-region control and topper layer synthesizing. Five classifiers were employed to the classification, included minimum distance classifier,Mahalanobis distance classifier,Bayes rule classifier,Fuzzy classifier and support vector machine. The result indicated that in the study area with the fragmentized distribution of forest,species and type diversity,complex structure,using the Bayes classifier,the total accuracies of the third level,the second level and the first level were 79. 38% ,81. 82% and 86. 98% respectively,where the third level contained twenty-two categories based on age-group of trees,the second level contained fifteen categories based on species,and the first level included nine categories based on species groups. With hierarchical classification,the result of upward synthesizing from lower levels to upper level was better than that classified from top levels to lower levels. Used as ancillary data,Landsat 7 ETM ＋ data were helpful to improve the classification accuracy of SPOT5 imagery. However,they could only be used to extract object features and couldn’t be involved in segmentation,as they would reduce the homogeneity and increase the heterogeneities of objects,and then affect the classification accuracy.