Spectral band selection for vegetation properties retrieval using Gaussian processes regression
This tool addresses the problem of efficiently identifying informative spectral bands for vegetation property retrieval, which is relevant for researchers and practitioners working with imaging spectrometers and remote sensing data.
This paper introduces an automated spectral band analysis tool (BAT) based on Gaussian processes regression (GPR) to identify the most informative bands for retrieving vegetation properties from spectral data. The GPR-BAT procedure sequentially removes the least contributing band until only one remains, aiming to find the minimal set of bands that maintain optimized prediction accuracy.
With current and upcoming imaging spectrometers, automated band analysis techniques are needed to enable efficient identification of most informative bands to facilitate optimized processing of spectral data into estimates of biophysical variables. This paper introduces an automated spectral band analysis tool (BAT) based on Gaussian processes regression (GPR) for the spectral analysis of vegetation properties. The GPR-BAT procedure sequentially backwards removes the least contributing band in the regression model for a given variable until only one band is kept. GPR-BAT is implemented within the framework of the free ARTMO's MLRA (machine learning regression algorithms) toolbox, which is dedicated to the transforming of optical remote sensing images into biophysical products. GPR-BAT allows (1) to identify the most informative bands in relating spectral data to a biophysical variable, and (2) to find the least number of bands that preserve optimized accurate predictions. This study concludes that a wise band selection of hyperspectral data is strictly required for optimal vegetation properties mapping.