Abstract:
This letter examines how the principal component analysis (PCA) affects the performance of the deep forest 2021 (DF21) model for the hyperspectral inversion. To this end,...Show MoreMetadata
Abstract:
This letter examines how the principal component analysis (PCA) affects the performance of the deep forest 2021 (DF21) model for the hyperspectral inversion. To this end, the spectra contaminated by eight types of heavy metals are applied and processed by PCA. Subsequently, various retained principal components (PCs) are devoted to establish the inversion model in line with the DF21. Two typical heavy metal elements, i.e., zinc (Zn) and chromium (Cr), are used for instance; it explores the accuracies of the DF21 model for inverting their concentrations under different PCs. The findings reveal that the DF21 model’s performance fluctuates with varying retained PCs. Furthermore, the fluctuations are influenced by the heavy metal’s type and its concentration distribution. As a result, the optimal performances for inverting the Zn and Cr concentrations appear at the first nine PCs and first eight PCs, respectively. Hence, it might not be feasible to improve the accuracy by retaining more PCs in hyperspectral inversion, at least not for the DF21 model.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)