Alternating least-squares unmixing for the extraction of sub-pixel information from agricultural areas

Laurent Tits; Ben Somers; Wouter Saeys; Pol Coppin

doi:10.1117/12.2028851

16 October 2013 Alternating least-squares unmixing for the extraction of sub-pixel information from agricultural areas

Laurent Tits, Ben Somers, Wouter Saeys, Pol Coppin

Proceedings Volume 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV; 888706 (2013) https://doi.org/10.1117/12.2028851
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

Alternating Least Squares (ALS) is a blind source separation method commonly used in Chemometrics to simultaneously estimate the absorption spectrum and concentration of the different components in a chemical sample. In this study, the transferability of ALS from Chemometrics to agricultural Remote Sensing is evaluated. Due to the subpixel contribution of background components, spectral unmixing has become an indispensable processing step in the spectral analysis of agricultural areas. Yet, traditional unmixing techniques only allow estimating the sub-pixel cover distribution of the different components, but fail to provide an estimate of the pure spectral signature of the crop. This info is, however, highly valuable as this pure crop signature could be used to monitor the health status of the trees. Here, we anticipate that ALS can provide a solution. ALS estimates both the concentration and the absorption spectra of the different components in a chemical sample and this can easily be translated into estimating both the subpixel cover fraction and spectral signature of the different components in a mixed image pixel. The ALS model was tested on simulated hyperspectral images of Citrus orchards in which ray-tracing software was used to realistically incorporate spectral variability, multiple scattering and shadowing effects. Both the accuracy of the extracted cover fractions and the pure spectral signatures of the crop were assessed, as well as the accuracy with which the biophysical parameters of the trees (i.e. chlorophyll content, leaf water content and Leaf Area Index) could be derived from the extracted crop signature. ALS indeed allowed to simultaneously estimate the subpixel cover distribution (RMSE = 0.05), as well as the pure spectral signatures of the different endmembers (RRMSE < 0.12), and considerably improved the extraction of biophysical parameters (ΔR2 up to 0.43). ALS thus provides a promising new image analysis tool for agricultural remote sensing.

Citation Download Citation

Laurent Tits, Ben Somers, Wouter Saeys, and Pol Coppin "Alternating least-squares unmixing for the extraction of sub-pixel information from agricultural areas", Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 888706 (16 October 2013); https://doi.org/10.1117/12.2028851

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