Effects of spectral transformations in statistical modeling of leaf biochemical concentrations

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI:10.1109/WARSD.2003.1295203

Runhe Shi, D. Zhuang, Z. Niu

引用次数: 10

Abstract

The prediction of leaf biochemical concentrations with hyperspectral data is one of latest research directions in hyperspectral remote sensing. Statistical modeling being a convenient and common-used method, spectral transformations are always performed as its preprocess. We discussed several usual transformations including full-band based transformations such as reciprocal, logarithm, and derivative spectra, and one-absorption-feature based transformation: continuum removal. The effects of those transformations on the prediction of C/N were compared using correlation analyses and stepwise regressions. Results show that the effect of continuum removal is the best, which is physically based and not site-specific at all.

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光谱变换对叶片生化浓度统计建模的影响

利用高光谱数据预测叶片生化浓度是高光谱遥感的最新研究方向之一。统计建模是一种方便且常用的方法，光谱变换通常作为其预处理。我们讨论了几种常用的变换，包括基于全波段的变换，如倒数、对数和导数光谱，以及基于单吸收特征的变换:连续体去除。利用相关分析和逐步回归分析比较了这些转换对碳氮比预测的影响。结果表明，连续体去除效果最好，这是基于物理的，而不是特定于部位的。

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IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003

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