Spectral band selection for vegetation properties retrieval using Gaussian processes regression

TL;DR

This paper presents GPR-BAT, an automated spectral band analysis tool using Gaussian processes regression, to identify the most informative spectral bands for vegetation property retrieval, improving spectral data processing efficiency.

Contribution

Introduction of GPR-BAT, a novel automated method for spectral band selection based on Gaussian processes regression, integrated into the ARTMO toolbox for vegetation analysis.

Findings

GPR-BAT effectively identifies key spectral bands for vegetation properties.

Optimal band selection enhances prediction accuracy with fewer bands.

Hyperspectral data band selection is crucial for accurate vegetation mapping.

Abstract

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.