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Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation


This paper reports a new approach for quantifying vegetation pigment concentrations through wavelet decomposition of hyperspectral remotely sensed data. Wavelets are a group of functions that vary in complexity and mathematical properties, that are used to dissect data into different frequency components and then characterize each component with a resolution appropriate to its scale. Wavelet analysis of a reflectance spectrum is performed by scaling and shifting the wavelet function to produce wavelet coefficients that are assigned to different frequency components. By selecting appropriate wavelet coefficients, a spectral model can be established between the coefficients and biochemical concentrations. Hence, wavelet analysis has the potential to capture much more of the information contained within high-resolution spectra than previous approaches and offers the prospect of developing robust, generic methods for pigment determinations. The capabilities of the wavelet-based technique were examined using reflectance spectra and pigment data collected for a range of plant species at leaf and canopy scales. For the combined data set and all of the individual vegetation types, methods based on wavelet decomposition appreciably outperformed narrowband spectral indices and stepwise selection of narrowband reflectance. However, there was variation between vegetation types in the relative performance of the three different feature extraction techniques employed for selecting the wavelet coefficients for use in predictive models. There was also considerable variability in the performance of predictive models according to the wavelet function used for spectral decomposition and the optimum wavelet functions differed between vegetation types and between individual pigments within the same vegetation type. The research indicates that wavelet analysis holds promise for the accurate determination of chlorophyll a and b and the carotenoids, but further work is needed to refine the approach.......

【作者名称】: G. A. BLACKBURN
【作者单位】: Department of Geography, Lancaster University, Lancaster, LA1 4YB, UK
【关 键 词】: Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation
【期刊名称】: International journal of remote sensing
【期刊论文数据库】: [DBS_Articles_01]
【期刊论文编号】: 108,901,680
【摘要长度】: 2,010
【上篇论文】: 外文期刊 - Mimetic biosensors composed by layer-by-layer films of phospholipid, phthalocyanine and silver nanoparticles to polyphenol detection
【下篇论文】: 外文期刊 - A web-based, relational database for studying glaciers in the Italian Alps

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