【作者】 邹丽丽；

【导师】 崔海山；

【作者基本信息】 广州大学 ， 土地资源管理， 2010， 硕士

【摘要】 随着多空间分辨率、多光谱分辨率、多传感器遥感数据日益增多,遥感数据融合技术已经在地学领域得到了广泛应用。本研究选取RADARSAT-1的SAR影像和SPOT多光谱影像,经过几何校正、辐射定标、降噪等预处理,采用PCA、BT、IHS、MT、HPF和WT六种方法融合,并利用融合后影像进行线性地物提取。主要研究内容及结论如下:⑴关于遥感数据融合方法的研究基于结构信息变换的HPF和WT方法,纹理结构信息保持较好,线性地物特征尤为突出;基于统计信息变换的PCA方法融合后影像,不仅能较好保持多光谱信息,而且也保持SAR影像纹理结构信息,信息熵指数最大;基于彩色变换IHS、BT和MT方法融合后影像,虽然视觉效果较理想,但是色彩扭曲以及光谱失真现象严重,信息熵值最小。六种融合后影像融合效果总体上都优于原始SPOT数据。⑵关于道路提取的研究谱间特征法提取道路,在漏分误差方面,PCA和HPF方法漏分情况较好;WT和BT方法提取出道路、漏分率次之;漏分率最大的是IHS方法,达到了43%,用同样方法检验SPOT多光谱影像道路漏分率。结果不尽人意,基本上有50%道路出现漏分现象。在错分误差方面,PCA和WT方法的错分率较低;BT和HPF方法次之; MT方法错分现象严重。SPOT多光谱影像错分率则有所降低,但是,错分率仍然低于六种融合后方法提取出的道路。最大似然法所提取道路,PCA和HPF道路提取效果较好道路特征明显,纹理清晰,错分和漏分现象不明显;BT和WT次之;BT和HIS错分和漏分现象严重,道路提取精度较差。但是,融合后数据所提取出来的道路精度明显比SPOT多光谱影像所提取的道路精度高。综上所述,经融合后的影像既保留原始SPOT多光谱信息,又保持SAR影像纹理结构信息,提高融合效果,达到提高线性地物提取精度要求,具有实际应用价值。更多还原

【Abstract】 With the rapid development of pace, computer and sensor technology, remote sensing data in geography fields has been widely used day by day. This paper uses the RADARSAT -1 satellite SAR image and the SPOT multispectral images, after geometric correction, radiometric calibration, noise reduction and other pretreatment, through PCA,BT,HIS,MT, HPF and WT six methods to study the image fusion ,then extract linear feature Main contents and conclusions as follows:⑴Study of Image Fusion MethodsThe results show that: based on a structural transformation image fusion HPF and WT methods ,the feature of liner clearly, and have the same color as the original multispectr- al ;The PCA based on statistical information images fusion methold , not only preserve the good texture information, but also maintain the SAR image texture information; Image based on IHS,BT and MT, although the visual effects are better, but the distortion and spectral distortion of information is serious, the entropy is lowest. But in general they are better than the original SPOT data.⑵Study of Extracting Road InformationExtract road information using spectral signature,In the field of leakaging points, PCA and HPF are better for road extraction; Followed by BT and WT; IHS’s leakaging poi- nts are serious as 43%. In the field of misclassification, PCA and WT’s misclassification is lower; Followed by BT and HPF; MT’s misclassification are serious.accuracy of the origin- al SPOT image which extracting the road is worst either leakaging or misclassification rate。Extract Road Information Using Maximum Likelihood,based on PCA and HPF fusion methods can retain the road linear features better and extract the road completely; Followed by BT and WT;MT’s misclassification and IHS’s leakaging and misclassification rate are serious.But,accuracy of the original SPOT image which extracting the road is lower than six fusion metholds.In summary, Based on the six fusion image, not only preserves the original SPOT multispectral information, while maintaining SAR image texture information to improve fusion results, to improve the accuracy of linear feature extraction, and their use value.更多还原