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Effect of Denoising in Band Selection for Regression Tasks in Hyperspectral Datasets
This paper presents a comparative analysis of six
band selection methods applied to hyperspectral datasets for
biophysical variable estimation problems, where the effect of
denoising on band selection performance has also been analyzed.
In particular, we consider four hyperspectral datasets and three
regressors of different nature ("�SVR, Regression Trees, and
Kernel Ridge Regression). Results show that the denoising
approach improves the band selection quality of all the tested
methods. We show that noise filtering is more beneficial for
the selection methods that use an estimator based on the whole
dataset for the prediction of the output than for methods that
use strategies based on local information (neighboring points)
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