摘要
受制于成像原理及制造技术等因素,航天高光谱遥感图像的空间分辨率相对较低,为此提出将高光谱图像与高空间分辨率图像进行融合处理,设计最佳的增强高光谱遥感图像空间分辨率的融合算法。针对地球观测1号(EO-1)Hyperion高光谱图像和高级陆地成像仪(ALI)全色波段图像的特点,从9种具体遥感图像融合算法中选用4种融合算法开展山区与城市的数据融合实验,即Gram-Schmidt光谱锐化融合法、平滑调节滤波(SFIM)变换融合法、加权平均法(WAM)融合法和小波变换(WT)融合法,并分别从定性、定量和分类精度三方面对这些方法的融合效果进行综合评价与对比分析,从而确定适合EO-1高光谱与全色图像融合的最佳方法。实验结果显示:从图像融合效果看,在所采用的4种融合方法中,Gram-Schmidt光谱锐化融合法的效果最好;从图像分类效果看,基于融合图像的分类效果要优于基于源图像的分类效果。理论分析与实验结果均表明:Gram-Schmidt光谱锐化融合法是一种较为理想的高光谱与高空间分辨率遥感图像的融合算法,为提高高光谱遥感图像的清晰度、可靠性及图像的地物识别和分类的准确性提供有力的支持。
Subject to the imaging principle, manufacturing technology and other factors, the spatial resolution of spaceborne hyperspectral remote sensing imagery is relatively low. Therefore, the thesis proposed the image fusion of hyperspectral imagery and high spatial resolution imagery, and designed the best fusion algorithm to enhance spatial resolution of hyperspectral remote sensing imagery. According to the characteristics of Earth Observing-1 （EO-1） Hyperion hyperspectral imagery and Advanced Land Imager （ALI） panchromatic imagery, 4 kinds of fusion algorithms were selected to carry out a comparative study of the image fusion effect for the city and mountain regions from 9 kinds of remote sensing image fusion algorithms, namely Gram-Schmidt spectral sharpening fusion method, transform fusion method of Smoothing Filter-based Intensity Modulation （SFIM）, Weighted Average Method （WAM） fusion method and Wavelet Transformation （WT） fusion method. And it carried out the comprehensive evaluation and analysis of the image fusion effect from 3 aspects of qualitative, quantitative and classification precision, which aims to determine the best fusion method for EO-1 hyperspectral imagery and panchromatic imagery. The experimental results show that： 1） from the image fusion effect, Gram-Schmidt spectral sharpening fusion method is the best in 4 kinds of fusion methods used; 2） from the image classification effect, the classification results based on the fusion image is better than the classification results based on the source image. The theoretical analysis and experimental results show that Gram-Schmidt spectral sharpening fusion method is an ideal fusion algorithm for hyperspectral imagery and high spatial resolution imagery, and it can provide powerful support to improve the clarity of hyperspectral remote sensing imagery, the reliability and the accuracy of the image object recognition and classification.
出处
《计算机应用》
CSCD
北大核心
2014年第8期2365-2370,共6页
journal of Computer Applications
基金
国家社会科学基金资助项目(03BTJ004)
福建省自然科学基金资助项目(2011J01265)
关键词
高光谱图像
数据融合
综合评价
地球观测1号
hyperspectral imagery
data fusion
comprehensive evaluation
Earth Observing-1 （EO-1）
