基于小波变换的遥感图像融合方法研究
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摘要
如何充分地利用全色图像的高空间分辨率信息和多光谱图像的光谱特性,使融合后的图像既具有高空间细节表现能力,又保留源多光谱图像丰富的光谱信息,是遥感图像融合领域的研究重点。传统的基于像素最大的融合方法,忽视了相邻像素间的相关性;基于区域方差最大的融合方法,虽考虑了区域特性,但忽视了像素所反映的结构特性。此外,这两种方法都只考虑一个特征量作为最后融合结果的判决依据,不够全面,因此都不能取得最优的融合效果。
基于上述背景,本文以遥感图像解译理论和小波多尺度多分辨率分析理论为技术基础,以SPOT全色图像和TM多光谱图像为实验数据,运用Matlab强大的矩阵运算功能进行图像信息的提取、融合及定量分析,通过小波多分辨率分析,对结合光谱信息和空间信息的遥感图像的融合算子和融合规则进行了较深入的研究,提出了基于区域结构相似度的融合算法,以及基于多尺度边缘增强的融合算法,并采用融合结果评价指标,将它们与传统的融合方法进行实验对比分析与评价。
How to make use of high-resolution image and multi-spectral image fully, to improve definition and resolution of the multi-spectral image and retain the color feature of multi-spectral image, is the key point in the remote sensing field. The conventional approach called pixel-based fusion only based upon the pixel level, which take no account of spatial correlativity between the neighborhood pixels. The other method called region-based variance fusion neglected the feature of pixel structure. In addition, both of them considered only one feature as the final fusion judging basis, which is not very comprehensive. So they both lead to not good fusion results.
In the background above, the dissertation is based on the remote-sensing image interpreter principal and wavelet MRA theory. Using the strongly matrix operation capability of Matlab software to extract the image information from TM and SPOT images ,and do the quantitative application. Integrated spatial and spectral information ,it proposed fusion methods called structrue similarity based on region and edge-enhanced fusion with wavelet's multi-resolution analysis. We apply these two algorithms to the typical cases of TM and SPOT, and compared the fusion results with the conventional approaches.
基于上述背景,本文以遥感图像解译理论和小波多尺度多分辨率分析理论为技术基础,以SPOT全色图像和TM多光谱图像为实验数据,运用Matlab强大的矩阵运算功能进行图像信息的提取、融合及定量分析,通过小波多分辨率分析,对结合光谱信息和空间信息的遥感图像的融合算子和融合规则进行了较深入的研究,提出了基于区域结构相似度的融合算法,以及基于多尺度边缘增强的融合算法,并采用融合结果评价指标,将它们与传统的融合方法进行实验对比分析与评价。
How to make use of high-resolution image and multi-spectral image fully, to improve definition and resolution of the multi-spectral image and retain the color feature of multi-spectral image, is the key point in the remote sensing field. The conventional approach called pixel-based fusion only based upon the pixel level, which take no account of spatial correlativity between the neighborhood pixels. The other method called region-based variance fusion neglected the feature of pixel structure. In addition, both of them considered only one feature as the final fusion judging basis, which is not very comprehensive. So they both lead to not good fusion results.
In the background above, the dissertation is based on the remote-sensing image interpreter principal and wavelet MRA theory. Using the strongly matrix operation capability of Matlab software to extract the image information from TM and SPOT images ,and do the quantitative application. Integrated spatial and spectral information ,it proposed fusion methods called structrue similarity based on region and edge-enhanced fusion with wavelet's multi-resolution analysis. We apply these two algorithms to the typical cases of TM and SPOT, and compared the fusion results with the conventional approaches.
引文
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