遥感数据像素级融合统一模型及实现技术

Generalized Model for Remotely Sensed Data Pixel-level Fusion and its Implementation Technology

研究遥感数据像素级融合统一模型及相应的实现技术具有重要的理论和应用价值。根据遥感数据成像机理和多光谱影像理想融合增强结果提出了遥感数据融合算法统一模型,该模型通过数学表达式的方式清晰地反映了参与融合的多光谱影像、从高分辨率全色影像提取的空间细节信息和采取的融合策略三者之间的运算关系,将三类典型融合算法推导成本文提出的统一模型;提出了基于统一模型的实现技术,该实现技术舍去对融合结果没有影响的计算步骤,只计算影响最终融合结果所需的参量,并列出常用融合算法实现过程中两个重要参量的求法。通过分析与比较,本文的统一模型概括更全面、适用性更广;通过分析实现步骤和比较实验结果,证明了基于统一模型的实现技术具有减少运算量的优越性,且具有普遍适用性,能应用于已提出的大部分遥感数据融合算法,有助于遥感数据融合技术的应用推广。

The generalized model characterizing most remotely sensed data pixel-level fusion techniques is very important for theory analysis and application. According to the imaging mechanism and the ideal pan-sharpening results of multispectral image,a generalized model for remote sensing data fusion is presented,which can clearly describe the mathematical relationship among original muhi-spectral image,the spatial details extracted from high-resolution panchromatic image, and the adopted fusion strategy. Also three types of fusion algorithm are translated into the generalized model using mathematical expression. The implementation technology based on the generalized model is developed, which only calculates the variables affecting the fusion results instead of all variables. Then these calculation methods of the two key variables are listed for most common pixel-level fusion algorithms. Compared to other models, the generalized model is comprehensive and adaptive. Analyzing implementation steps and comparing with the regular fusion results, the implementation technology which can be applied to most fusion algorithms can reduce computation complexity and save calculation time, which is helpful to promote application of remote sensing data fusion.