面向对象分类技术在高分辨率遥感影像信息提取中的应用研究

Application Research on Information Extraction Based on High-Resolution Remote Sensing Images Using Object - Oriented Analysis

应用高分辨率遥感数据进行矿山环境监测是近几年来矿山监测工作的一个发展趋势,信息提取技术是遥感数据应用中的关键。传统的像元分类方法只考虑了光谱信息,信息提取量少,分类精度低,难以满足高分辨率数据信息的提取。本文以IKONOS影像为数据源,利用面向对象分类新技术,探讨该技术在矿山高分辨遥感数据中的应用。最后运用kappa系数比较评价面向对象分类方法与传统的像元分类方法。研究表明,面向对象分类法比基于像元分类法精度更高,效果更好,具有较好的应用前景。

The application of high resolution remote sensing data for environmental monitoring mine is a development trend in recent years of monitoring work.Information extraction technology is the key in the application of remote sensing data.The traditional pixel classification method just considers spectral information,so the quantity of information extraction is small and the classification accuracy is low,which is difficult to meet the high resolution data information extraction.This article takes the IKONOS as the data source,applying object-oriented classification technology for application of mine area information extraction based on remote sensing images.At last,the paper compares and evaluates the effect of object-oriented analysis and pixel information extraction methods using kappa coefficient.The results show that the precision of object-oriented classification is higher and better than the pixel-based classification with better application prospect.