Study on Object-oriented Remote Sensing Image Classification Based on Multi-levels Segmentation
基于多层分割的面向对象遥感影像分类方法研究

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.