Genetic Algorithm Captured the Informative Bands for Partial Least Squares Regression Better on Retrieving Leaf Nitrogen from Hyperspectral Reflectance
Genetic Algorithm Captured the Informative Bands for Partial Least Squares Regression Better on Retrieving Leaf Nitrogen from Hyperspectral Reflectance
复制标题
遗传算法在从高光谱反射率中反演叶片氮时更好地捕获了偏最小二乘回归的信息带
DOI：
10.3390/rs14205204
复制
发表时间：
2022-10
期刊：
影响因子：
5
通讯作者：
Quan Wang
中科院分区：
文献类型：
--
作者：
Jia Jin;Mengjuan Wu;Guangman Song;Quan Wang
氮素是调节植物生理过程的主要营养元素。虽然各种偏最小二乘回归（PLSR）模型已被提出来估计叶片氮含量（LNC）从高光谱数据具有良好的精度，不幸的是，他们是不强大的，往往不适用于新的数据集，超越他们被开发。据报道，选择信息带对于改进PLSR模型的性能并提高其对于一般应用的鲁棒性至关重要。然而，尚未就最佳波段选择方法达成共识，因为校准和验证数据集通常仅限于样本量较小的少数物种。在这项研究中，我们解决了这个问题的基础上，一个相对全面的联合数据集，包括模拟数据集产生的最近开发的叶尺度辐射传输模型（PROSPECT-PRO）和两个公共的在线数据集，评估不同的信息波段选择技术的信息波段选择。结果表明，结合适当的波段选择方法可以大大提高PLSR模型估计LNC的拟合优度，而不是使用全波段。利用遗传算法（GA）和无信息变量剔除（UVE）方法筛选出的信息条带，对模拟数据集进行PLSR模型校正，得到的模型对两个独立的实测数据集的LNC也是可靠的。特别是，遗传算法是更有效地捕捉信息波段从高光谱数据检索LNC。这些研究结果将为建立强大的PLSR模型从高光谱遥感数据中检索LNC提供有价值的见解。
Nitrogen is a major nutrient regulating the physiological processes of plants. Although various partial least squares regression (PLSR) models have been proposed to estimate the leaf nitrogen content (LNC) from hyperspectral data with good accuracies, they are unfortunately not robust and are often not applicable to novel datasets beyond which they were developed. Selecting informative bands has been reported to be critical to refining the performance of the PLSR model and improving its robustness for general applications. However, no consensus on the optimal band selection method has yet been reached because the calibration and validation datasets are very often limited to a few species with small sample sizes. In this study, we address the question based on a relatively comprehensive joint dataset, including a simulation dataset generated from the recently developed leaf scale radiative transfer model (PROSPECT-PRO) and two public online datasets, for assessing different informative band selection techniques on the informative band selection. The results revealed that the goodness-of-fit of PLSR models to estimate LNC could be greatly improved by coupling appropriate band-selection methods rather than using full bands instead. The PLSR models calibrated from the simulation dataset with informative bands selected by genetic algorithm (GA) and uninformative variable elimination (UVE) method were reliable for retrieving the LNC of the two independent field-measured datasets as well. Particularly, GA was more effective to capture the informative bands for retrieving LNC from hyperspectral data. These findings should provide valuable insights for building robust PLSR models for retrieving LNC from hyperspectral remote sensing data.
登录
查看更多内容
DOI：
10.3390/rs13081428
发表时间：
2021-04
期刊：
Remote. Sens.
影响因子：
--
作者：
I. Marang;P. Filippi;T. Weaver;B. Evans;B. Whelan;T. Bishop;Mohammed O. F. Murad;Dhahi Al-Shammari;G. Roth
通讯作者：
I. Marang;P. Filippi;T. Weaver;B. Evans;B. Whelan;T. Bishop;Mohammed O. F. Murad;Dhahi Al-Shammari;G. Roth
影响因子：
13.5
作者：
B. Becker;D. Lusch;J. Qi
通讯作者：
B. Becker;D. Lusch;J. Qi
影响因子：
5.1
作者：
S. Rubo;J. Zinkernagel
通讯作者：
S. Rubo;J. Zinkernagel
影响因子：
13.5
作者：
Feret, J. -B.;Gitelson, A. A.;Jacquemoud, S.
通讯作者：
Jacquemoud, S.
影响因子：
5
作者：
Deepak, Maya;Keski-Saari, Sarita;Keinanen, Markku
通讯作者：
Keinanen, Markku