面向对象的森林植被图像识别分类方法

 森林植被信息提取是遥感影像分类中的难点,仅利用光谱信息难以提取森林植被的类型,本文以门头沟区森林植被占主要土地覆被类型为研究对象,选择HJ-1影像面向对象提取不同地物信息。由于研究区地形复杂,采用多尺度分割方法,对不同地物设置不同分割参数,实现不同地物分层提取。根据光谱、纹理及几何等特征选择合适的特征参数,构建隶属度函数,逐级提取研究区的土地覆被类型,并与传统的最大似然法进行对比。结果表明:面向对象的分类方法在门头沟区森林植被二级信息提取的精度为83%,与传统方法相比有了较大的提高。

Study on Forest Classification Based on Object Oriented Techniques

Since vegetation is an important indicator of global climate change,then the way to extract vegetation changing data should be put as the top priority.Especially,the extraction of sub-category information of forest vegetation has always been a difficult point in remote sensing image classification.And it is more difficult to extract sub-category information of the forest vegetation type only by taking advantage of the spectral information.As a widely-used method,object-oriented classification has been rapidly developed from the beginning of this century.Object-oriented classification method is mainly used in high-resolution remote sensing imagines,and it is applicable to medium resolution remote sensing images.This paper took Mentougou District,Beijing,which is mainly covered with forest vegetation,as the object of this research,and took HJ-1 image as the main data source then different buildings can be extracted by using the object-oriented classification method.By the reason of complicated terrain in this district,a hierarchical segmentation method was proposed in this research.Then different segmentation parameters could be set according to different buildings.Based on the spectral characteristic of the vegetation,appropriate characteristic parameters could be chosen and subordination function is constructed.After then,land cover types in this district could be extracted step by step and at the same time could be compared with those by the traditional maximum likelihood method.The result indicates that extraction accuracy of the forest vegetation sub-category data in this Mentougou District is 83% by using the object-oriented classification method.Compared with the traditional method,the extraction accuracy has been boosted a lot.