光谱分解中的误差传播分析

M. Sharp
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引用次数: 0

摘要

线性混合模型(LMM)通常用于计算图像像素中材料的相对分数。与任何线性方法一样,数据和转换中的错误会在解决方案中传播。本文给出了数据误差如何影响LMM解的解析推导。我们证明了对于简单类型的输入误差,解误差可以表示为端元的函数。我们给出了约束解和无约束解的2和3端元问题的例子。结果表明,分数误差与无约束解中端元之间的谱角成反比,与约束解中端元之间的欧几里得距离成反比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of error propagation in spectral unmixing
The linear mixing model (LMM) is often used to compute the relative fractions of the materials in an image pixel. As in any linear method, errors in the data and transformations are propagated in the solution. This paper presents an analytical derivation showing how error in the data affects the solution of the LMM. We show that for simple types of input error, the solution error can be expressed as a function of the end members. We present examples of 2 and 3 end member problems for both the constrained and the unconstrained solution. We show that the error in the fractions is inversely related to the spectral angle between end members in the unconstrained solution, and inversely related to the Euclidean distance between end members in the constrained solution.
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