首页|Liaoning Technical University Researcher Illuminates Research in Machine Learning (Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression)
Liaoning Technical University Researcher Illuminates Research in Machine Learning (Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on artificial intelligence. According to news reporting out of Fuxin, People’s Republic of China, by NewsRx editors, research stated, “As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accurately.” Financial supporters for this research include National Natural Science Foundation of China; China Postdoctoral Science Foundation. Our news correspondents obtained a quote from the research from Liaoning Technical University: “Therefore, many studies have aimed to address this issue by proposing machine learning models that accurately invert forest canopy height. However, most of the these approaches feature PolSAR observa- tions from a data-driven viewpoint in the feature selection part of the machine learning model, without taking into account the intrinsic mechanisms of PolSAR polarization observation variables. In this work, we evaluated the correlations between eight polarization observation variables, namely, T11, T22, T33, total backscattered power (SPAN), radar vegetation index (RVI), the surface scattering component (Ps), dihedral angle scattering component (Pd), and body scattering component (Pv) of Freeman-Durden three- component decomposition, and the height of the forest canopy. On this basis, a weighted inversion method for determining forest canopy height under the view of structural equation modeling was proposed. In this study, the direct and indirect contributions of the above eight polarization observation variables to the forest canopy height inversion task were estimated based on structural equation modeling. Among them, the indirect contributions were generated by the interactions between the variables and ultimately had an impact on the forest canopy height inversion. In this study, the covariance matrix between polarization variables and forest canopy height was calculated based on structural equation modeling, the weights of the variables were calculated by combining with the Mahalanobis distance, and the weighted inversion of forest canopy height was carried out using PSO-SVR. In this study, some experiments were carried out using three Gaofen-3 satellite (GF-3) images and ICESat-2 forest canopy height data for some forest areas of Gaofeng Ridge, Baisha Lizu Autonomous County, Hainan Province, China.”
Liaoning Technical UniversityFuxinPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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