摘要
利用ALOS数据,在Definiens Developer 7软件中用分形网络演化法(FNEA)进行多级分割,获取影像对象。综合运用对象的光谱、空间特征和不同层对象之间的关系,提取了湖北省洪湖市试验区土地覆盖与土地利用信息。最后,用一种基于单层分割的面向对象分类方法和基于像素的最大似然法与这种基于多级分割的面向对象分类方法进行了对比分析。结果表明,基于多级分割的面向对象分类方法,不仅克服了基于像素的最大似然法出现的"椒盐"现象,在分类精度上较这两种分类方法也有大幅度的提高。
ALOS image data was used to carry out a multi-levels segmentation with a method called FNEA in Definiens Developer 7 solftware and image objects were got.Spectral and spatial values of image objects,as well as relationship of objects among different levels were considered to extract land use and land cover information in the test area located in Honghu City,Hubei Province.Then an object-oriented classification based on single level segmentation and a pixel-based Maximum Likelihood classification were used to compare with it.Results showed that the object-oriented classification based on multi-levels segmentation not only overcame ＂Pepper and Salt Phenomenon＂ appeared in the pixel-based Maximum Likelihood classification but also obtained a significant improvement on classification accuracy compared with the other two classification methods.
出处
《遥感技术与应用》
CSCD
北大核心
2010年第1期149-154,共6页
Remote Sensing Technology and Application
基金
国家自然科学基金重点项目(40730635)
水利部公益项目(200701024)资助
关键词
面向对象
多级分割
模糊函数
分类
ALOS影像
Object-oriented
Multi-levels segmentation
Fuzzy function
Classification
ALOS image
