Multispectral Image Classification Based on SubRegion and Hierarchical Theory
结合自动分区与分层分析的多光谱遥感图像地物分类方法

The development of remote sensing has offered abundant observing the data to mankind. And a trend of quantitative analysis of remotely sensed information is gradually realizing information extracted from the image automatically. It is not only the demand of the remote sensing application but also the advance of the remote sensing self-development. There is a very important demand at present in automatic classification of topographical objects by utilizing the remotely sensed image. However, automatic classification based on computer systems is still a challenge field because of the limitations of the complexity of natural conditions and remote sensing technology itself. The aim of this paper is to study classification approach of topographical objects with Multispectral image. In this thesis, a new method is presented, which is based on Sub-area and Hierarchical theory and consists of two parts. The first part is image automatic Sub-area based on spectrum analysis of topographical objects. The second part is multi-layers extracting of topographical objects with data of the spectrum characteristics before PCA and after PCA. In terms of the new method, a Hierarchical classification algorithm is developed, then applied to the classification of topographical objects in the partial region of Daqing. The experimental result shows the classification accuracy by our approach is obviously higher than the results by the traditional approaches.