摘要
遥感是快速获取土地利用/土地覆盖现势信息的重要手段之一。由于不同类型的卫星传感器获取同一地区的遥感数据日益增多，对不同平台、不同光谱响应范围的遥感数据源，用常规的遥感图像融合方法如 HSI 变换法、Brovey 变换法和 PCA 变换法进行融合时，存在不同程度的光谱扭曲现象，即融合后图像的色彩与多光谱图像存在较大差异，从而影响了地物的识别。针对不同平台、不同光谱响应范围的遥感数据，本文在 HSI 变换的基础上，提出了一种改进的方法，即，首先对高几何分辨率的全色波段进行 LoG 滤波，然后将 LoG 滤波后的全色波段与多光谱经 HSI 正变换后的强度分量，进行灰度直方图匹配，并替换之，经 HSI 逆变换便得到融合图像。分析结果表明：LFF融合法的光谱保持性能优于 HSI 变换法，LFF 融合后图像的分类精度高于 HSI 融合后的图像，LFF 融合法是一种能较好保持光谱特性的融合方法。
Remote sensing technology is a highly efficient method applied to investigate rapid land use change.We might acquire detailed land use situation from the fused production of multi-source RS data at least cost.These conventional fusion methods such as HSI transform,Brovey transform and principal components transformcould merge two optical image data of different resolutions——a high spatial resolution panchromatic image anda multi-spectral image at low spatial resolution.However,these fusion methods required same or analogousspectral response range between the high spatial resolution panchromatic image and the low spatial resolutionmulti-spectral image.This paper brought forward an improved fusion method called LoG Filter Fusion(LFF)based on HSI transform,that could merge different kinds of optical image data with different spectral responseranges of multi-source RS data.LFF arithmetic was put forward in this paper,that is,firstly to filter on the highspatial resolution panchromatic image with LoG filter,and then to substitute the intensity component throughHSI transform to engender the LFF image by HSI invert transform.The result of analysis on these fused imagesshowed the gray variance index of LFF fusion method is higher than HSI transform,the color of LFF image isanalogous to the low spatial resolution multi-spectral image,so the LFF fusion method is more excellent at thespectrum preservation capability than HSI transform.And the classification accuracy of the LFF image is 5.68%higher than HSI image.
出处
《地球信息科学》
CSCD
2004年第3期94-98,F002,共6页
Geo-information Science
关键词
土地利用
遥感
保持光谱
融合
land use
remote sensing
spectral preservation
fusion
