面向对象的优势树种类型信息提取技术

An object-based information extraction technology for dominant tree species group types

森林植被优势树种类型信息的提取是遥感影像分类中的难点.面向对象分类方法是用高空间分辨率遥感数据实现精确类型信息提取的新方法.本文以2013年Quickbird影像作为基础数据,选择福建省三明市将乐林场为研究区,采用面向对象多尺度分割方法提取耕地、灌草地、未成林造林地、马尾松、杉木和阔叶树等类型信息.分类特征融合植被的光谱、纹理和多种植被指数3类特征信息,建立类层次结构,对不同层次分别用隶属度函数和决策树分类规则,最终完成分类,并与只用纹理与光谱特征相结合的方法进行对比.结果表明:融合纹理、光谱、多种植被指数的面向对象的分类方法提取研究区优势树种类型信息的精度为91.3%,比只用纹理和光谱的方法精度提高了5.7%.

Information extraction for dominant tree group types is difficult in remote sensing image classification, howevers, the object-oriented classification method using high spatial resolution re- mote sensing data is a new method to realize the accurate type information extraction. In this paper, taking the Jiangle Forest Farm in Fujian Province as the research area, based on the Quickbird im- age data in 2013, the object-oriented method was adopted to identify the farmland, shrub-herba- ceous plant, young afforested land, Pinus massoniana, Cunninghamia lanceolata and broad-leave tree types. Three types of classification factors including spectral, texture, and different vegetation indices were used to establish a class hierarchy. According to the different levels, membership func- tions and the decision tree classification rules were adopted. The results showed that the method based on the object-oriented method by using texture, spectrum and the vegetation indices achieved the classification accuracy of 91.3%, which was increased by 5.7% compared with that by only using the texture and spectrum.