基于光谱响应函数的遥感图像融合对比研究

遥感图像融合是一个十分重要的问题,目前已出现了很多融合方法.一些现有方法可以从高空间分辨率全色数据中提取细节特征,并注入到低空间分辨牢多光谱数据中,同时尽可能保持多光谱数据的光谱特性.现有方法一般都不能利用遥感成像系统的物理信息,因此可能导致融合结果发生严重的光谱扭曲.该文采用适当的遥感图像融合模型对图像融合问题进行分解,将融合问题归结为空间细节调制参数构建与空间细节信息提取两个子问题,在对传感器光谱响应函数(SRF)的分析基础上,构建合理的空间细节调制参数.依据对现有方法的分类,文章将三种基于SRF的空间细节调制参数构建方法,与高斯高通滤波提取的空间细节信息结合,产生三种基于SRF的遥感图像融合方法.这些方法在Ikonos数据上进行了试验和分析,并与GS和HPM方法进行对比.

Comparison among remotely sensed image fusion methods based on spectral response function

Remotely sensed image fusion is a critical issue, and many methods have been developed to inject features from a high spatial resolution panchromatic sensor into low spatial resolution multi-spectral images, trying to preserve spectral signatures while improving spatial resolution of multi-spectral images. However, no explicit physical information of the detection system has been taken into account in usual methods, which might lead to undesirable effects such as severe spectral distortion. Benefiting from the proper decomposition of the image fusion problem by a concise image fusion mathematical model, the present paper focuses on comparing reasonable modulation coefficient of spatial details based on analysis of the spectral response function (SRF). According to the classification of former methods, three modulation coefficients based on SRF of sensors were concluded, which lead to three image fusion methods incorporating spatial detail retrieved by Gaussian high-pass filter. All these methods were validated on Ikonos data compared to GS and HPM method.