Derivative analysis of hyperspectral data
Derivative analysis of hyperspectral data
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DOI：
10.1117/12.262471
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发表时间：
1996-12
期刊：
影响因子：
--
通讯作者：
F. Tsai;W. Philpot
中科院分区：
文献类型：
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作者：
F. Tsai;W. Philpot
为了将导数光谱分析应用于高分辨率、光谱连续的遥感数据的分析，对几种平滑和导数计算算法进行了综述和改进，以开发一套跨平台的光谱分析工具。重点是探索不同的平滑和导数算法，以从任何连续的光谱数据集中提取细微的光谱特征。通过对带宽和采样间隔(频带分离)的交互选择，该算法可以优化降噪并更好地匹配感兴趣的光谱特征的尺度。使用实验室光谱数据来测试所实现的导数分析模块的性能。在合成光谱和大豆荧光光谱上执行了一种检测吸收带位置的算法，以演示所实现的模块在提取光谱特征中的使用。通过对所开发模块的检查，还观察和讨论了与平滑和由平滑或导数计算算法引起的频谱偏差有关的问题。深入研究了有限近似导数算法中频带分离迁移引起的尺度效应，以了解尺度效应与噪声去除之间的关系。
With the goal of applying derivative spectral analysis to analyze high resolution, spectrally continuous remote sensing data, several smoothing and derivative computation algorithms have been reviewed and modified to develop a set of cross-platform spectral analysis tools. Emphasis was placed on exploring different smoothing and derivative algorithms to extract subtle spectral features from any continuous spectral data sets. With interactive selection of bandwidth and sampling interval (band separation), the algorithm can optimize noise reduction and better match the scale of spectral features of interest. Laboratory spectral data were used to test the performance of the implemented derivative analysis modules. An algorithm for detecting the absorption band positions was executed on synthetic spectra and a soybean fluorescence spectrum to demonstrate the usage of the implemented modules in extracting spectral features. Upon examination of the developed modules, issues related to the smoothing and the spectral deviation caused by the smoothing or derivative computation algorithms were also observed and discussed. The scaling effect resulting from the migration of band separations when using the finite approximation derivative algorithm was thoroughly inspected to understand the relationship between the scaling effect and noise removal.