Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data
Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data
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Date
2011-12-04
Authors
Abdel-Rahman, Elfatih
Ahmeda, Fethi
Ismaila, Riyad
Journal Title
Journal ISSN
Volume Title
Publisher
uofk
Abstract
Nitrogen (N) is one of the most important limiting nutrients for sugarcane production.
Conventionally, sugarcane N concentration is examined using direct methods such as
collecting leaf samples from the field followed by analytical assays in the laboratory.
These methods do not offer real-time, quick, and non-destructive strategies for estimating
sugarcane N concentration. Methods that take advantage of remote sensing,
particularly hyperspectral data, can present reliable techniques for predicting sugarcane
leaf N concentration. Hyperspectral data are extremely large and of high dimensionality.
Many hyperspectral features are redundant due to the strong correlation between
wavebands that are adjacent. Hence, the analysis of hyperspectral data is complex and
needs to be simplified by selecting the most relevant spectral features. The aim of this
study was to explore the potential of a random forest (RF) regression algorithm for
selecting spectral features in hyperspectral data necessary for predicting sugarcane leaf
N concentration. To achieve this, two Hyperion images were captured from fields of
6–7 month-old sugarcane, variety N19. The machine-learning RF algorithm was used
as a feature-selection and regression method to analyse the spectral data. Stepwise
multiple linear (SML) regression was also examined to predict the concentration of
sugarcane leaf N after the reduction of the redundancy in hyperspectral data. The
results showed that sugarcane leaf N concentration can be predicted using both non–
linear RF regression (coefficient of determination, R2 = 0.67; root mean square error
of validation (RMSEV) = 0.15%; 8.44% of the mean) and SML regression models
(R2 = 0.71; RMSEV = 0.19%; 10.39% of the mean) derived from the first-order derivative
of reflectance. It was concluded that the RF regression algorithm has potential for
predicting sugarcane leaf N concentration using hyperspectral data
Description
This paper had been presented for promotion at the university of Khartoum. To get the full text please contact the author
elfatihabdelrahman@gmail.com
Keywords
Random,forest,regression,spectral,band,selection,estimating,sugarcane,leaf,nitrogen,concentration,EO-1,Hyperion,hyperspectral,data