中文摘要：

针对城镇绿化植被受可用土地限制,具有分布零散、结构复杂、植被群类型繁多等特点,以及建筑物和其他设施的阴影遮挡等加剧了植被群分类的困难,经典分类方法常常难以适应需求的困境,该文提出了以分层分割/分类和多描述符空间分类对城镇绿化植被群分类的方法,即采用一次采样,多层分类,利用分类要素间的层次关系,从粗到精依次分类,以降低分类难度和提高精度。通过对这些图斑做层之间关系的逻辑分析,以精度较高的边界取代较低者,可以得到精度较好的目标类图斑。此种方法可用于改善草地、乔灌木、阔叶植被、针叶植被、落叶植被、常绿植被等植被群之间的可分性。仿真测试表明,该算法对于高分辨遥感图像城镇绿化植被群分类具有一定的普适性,划分植被群的总体精度高于85%,比经典单层分类方法提高了5%~10%。

英文摘要：

Constrained by limited urban land resources and the needs in artificial aesthetics,urban landscape vegetation is often characterized by scattered distribution,complex structure,various species and the shaded scene resulting in the severe challenges to the classification of vegetation population from remotely sensed imagery.To solve this problem,this paper presents an algorithm to classify vegetation populations such as the lawn,trees,broadleaf,conifers,deciduous and evergreen plants by using hierarchical classification (HC)in multi-descriptor space,that is,conducting multi-layer classifications in accordance with the hierarchical relationship of the classes and using different combinations of mathematical descriptors for the classifications. For example,the two classes of with and with no vegetation cover will firstly be separated.This will be followed by the classification between grass and trees (and/or shrubs)from the previous vegetation-covered class.After this the separation between broadleaf and conifers plants or between deciduous and evergreen plants from the above tree/shrub classes can be reached.By using the inheritance relationship between these classes in different layers,the accuracy of classification can further be improved because the boundary of a patch of a class in a certain layer can be replaced with a more accurate one of the same class in another layer.The whole algorithm was tested by MATLAB simulation.It is revealed that the overall accuracy (OA)is about 85% by using HC and has 5% to 10% better than that by using conventional single-layer classification.