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标题：Identification of Conifer Species Based on Laboratory Spectroscopy and an Artificial Neural Network
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作者：Xin Wang ; Yi Zeng ; Shaohuang Wang 等
期刊名称：Journal of Software Engineering
印刷版ISSN：1819-4311
电子版ISSN：2152-0941
出版年度：2015
卷号：9
期号：2
页码：362-372
DOI：10.3923/jse.2015.362.372
出版社：Academic Journals Inc., USA
摘要：Remote identification of individual tree species contributing to a forest’s ecosystem is essential for the proper utilization and protection of our forest resources. In this study, we propose a novel neural network method, termed LAP-BP (which modifies the standard BP algorithm using the Laplace Transform function) to be able to discern individual tree species from one another based on their spectral imaging. To accomplish this, hyperspectral data (with a spectral range of 350~2,500 nm) in combination with the modified LAP-BP method was used in the identification of five common coniferous species endemic to China. Results show that when the value of the hidden layer of the neural network , in conjunction with training objective, is (6, 9) that LAP-BP network achieves the highest accuracy in coniferous species identification with a prediction accuracy of 86.78%. The differences of leaf thickness and shape among the five coniferous species are the primary factors contributing to the different spectral separabilities. This study offers a reference for new ideas and a novel methodology in coniferous species identification.