利用快速傅立叶变换表征热带植物的光谱特征

A. Ballado, J. Lazaro, Glenn O. Avendaño, Clarice An Rosette M. De Claro, Gabriel Kristofer Sandoval, Rez A. Viloria
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引用次数: 0

摘要

在植物叶片光谱特征的分类中,已经采用了不同的方法来获得一般的结果。有不同的光谱特征,叶片可能拥有的每个读数使用标准和校准在现场。这些是在热带地区发现的不同热带植物的光谱特性。本研究的重点是在叶片上发现的光谱读数和光谱特征的变化,特别是生长在干旱地区、潮湿地区和靠近海洋地区的植物。这项研究使用了一种机制,显示了这些植物的光谱特征如何根据它们可以找到的位置而变化。本研究将快速傅立叶变换算法应用于利用MATLAB对采集到的热带植物光谱数据进行分析。通过FFT处理的反射率显示了每个位置的每个植物的光谱特征是如何不同的。以下值为各植物的Reflectance - FFT结果的峰值:香蕉0.9466 @190Hz(干旱区),1.112 @190Hz(近海)和1.5408 @188Hz(湿地);芒果:0.723 @189Hz(干旱地区),1.2401 @189Hz(靠近大海)和1.634 @183Hz(潮湿地区);椰子:1.1278 @194Hz(干旱地区),0.8246 @194Hz(靠近大海)和1.0816 @194Hz(潮湿地区);鳄梨:0.8164 @194Hz(干旱地区),0.3852 @194Hz(近海地区),0.9661 @190Hz(潮湿地区);菠萝:0.654 @189Hz(干旱地区),1.3586 @189Hz(靠近大海)和1.524 @183Hz(潮湿地区)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characterization using spectral signature of tropical plants through fast fourier transform
In classifying plant leaves' spectral signature, different approach were already taken to achieve the general results. There are different Spectral Signatures that a leaf may possess on each reading using the standard and calibrated in the field. These are Spectral properties of different tropical plants found in tropical regions. This study focus on the spectral readings and variations in the spectral signatures found on leaf specifically plants that grows on Arid lands, Wet lands, and Near the Sea. The study uses a mechanism that shows how Spectral Signatures of these plants may vary in readings based on the location where they can be found. This study applies the algorithm known as Fast Fourier Transform in analyzing the gathered spectral data from tropical plants using MATLAB. The processed Reflectance through FFT shows how the Spectral Signature of each plant per location are different from each other. The following values is the peak values of Reflectance — FFT results for each plant: Banana 0.9466 @190Hz (Arid land), 1.112 @190Hz (Near the Sea) and 1.5408 @188Hz (Wet land); Mango: 0.723 @189Hz (Arid land), 1.2401 @189Hz (Near the Sea) and 1.634 @183Hz (Wet land); Coconut: 1.1278 @194Hz (Arid land), 0.8246 @194Hz (Near the Sea) and 1.0816 @194Hz (Wet land); Avocado: 0.8164 @194Hz (Arid land), 0.3852 @194Hz (Near the Sea) and 0.9661 @190Hz (Wet land); Pineapple: 0.654 @189Hz (Arid land), 1.3586 @189Hz (Near the Sea) and 1.524 @183Hz (Wet land).
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