基于影像融合和面向对象技术的植被信息提取研究

高分辨率影像具有丰富的光谱信息和空间信息。采用不同的图像融合技术融合GeoEye影像全色波段和多光谱波段,用建立的参考多边形和对应多边形残差法评价分割质量,以确定研究区各地物类型的最优分割参数组合,选择目标地物分类特征,建立分类规则,在此基础上实现研究区内不同地物类型的面向对象信息提取。结果表明:Gram-Schmidt(GS)融合法具有最优的融合效果,所选特征能够很好地实现目标地物信息提取,并且具有明确的地学意义,面向对象信息提取总体精度达到90.3%,Kappa系数为0.86,该研究为高精度植被信息的提取提供了有效的方法。

Study on Vegetation Information Extraction based on Object-oriented Image Analysis

Vegetation is an important part in ecological system and indicating certain landscapes,It is a meaningful work to obtain detailed information of vegetation using GeoEye image with its abundant spatial and spectral information.This study fused the panchromatic band and multispectral bands with four image fusion methods,Image segmentation is the first and critical procedure in the workflow of object\|oriented image analysis,discrepancy between reference polygons and corresponding segment is used to assess segmentation quality in this study.We extracted the vegetation information using classification feature which is selected from the perspective of remote sensing image cognition and geographical understanding.The results showed that Gram\|Schmidt(GS)method is the most effective in fusing panchromatic bands and multispectral bands,And object\|oriented classification is effective in high resolution remote sensing information extraction,the overall accuracy is up to 90.3%.this research provided an effective method for vegetation information extraction.