Estimating chlorophyll content of Zizania latifolia with hyperspectral data and random forest

Abstract

The amount of chlorophyll in a plant useful to indicate its physiological activity and then changes in chlorophyll content have been used as a good indicator of disease as well as nutritional and environmental stresses on plants. Chlorophyll content estimation is one of the most applications of hyperspectral remote sensing data. The aim of this study is to evaluate dimensionality reduction for estimating chlorophyll contents from hyperspectral reflectance. Random Forest (RF) has been applied to assess biochemical properties such as chlorophyll content from remote sensing data; however, an approach integrating with dimensionality reduction techniques has not been fully evaluated. A total of 200 Zizania latifolia leaves with 5 treatments from Shizuoka University field were measured for reflectance and chlorophyll content. then, the regression models were generated based on RF with three dimensionality reduction methods including principal component analysis, kernel principal component analysis and independent component analysis. This research clarified that PCA is the best method for dimensionality reduction for estimating chlorophyll content in Zizania Latifolia with a RMSE value of 5.65 ± 0.58 μg cm -2 .

Effect of denoising methods for hyperspectral images classification: DnCNN, NGM, CSF, BM3D and Wiener

“…The number of photons captured per band is much lesser in HSIs than in RGB images, allowing different noises to be easily incorporated into the corresponding bands during the acquisition process. This is because HSIs capture the spectral information of each spatial location in a scene with large wavebands [1][2][3]. These noises cause image distortions, which have a detrimental effect on the performance of all HSI applications as well as how the HSI is visually presented.…”

Deducing Leading Factors of Spatial Distribution of Carbon Reserves in Nanjing Metropolitan Area Based on Random Forest Model

“…Compared with the other methods, the random forest is suitable for processing high-dimensional data, is not easy to generate overfitting, and performs well when processing the data [ 31 , 33 , 34 ]. At present, RF model has been widely used in medicine [ 35 , 36 ], economics [ 37 , 38 ], remote sense [ 39 , 40 ], and some other fields, but it has not been widely used in the research of carbon reserves.…”