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Open AccessArticle

Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth

1
Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA
2
Boeing Research and Technology, Huntsville, AL 35824, USA
3
Department of Electrical & Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1614; https://doi.org/10.3390/rs11131614
Received: 12 May 2019 / Revised: 28 June 2019 / Accepted: 30 June 2019 / Published: 8 July 2019
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Abstract

An important application of airborne- and satellite-based hyperspectral imaging is the mapping of the spatial distribution of vegetation biophysical and biochemical parameters in an environment. Statistical models, such as Gaussian processes, have been very successful for modeling vegetation parameters from captured spectra, however their performance is highly dependent on the amount of available ground truth. This is a problem because it is generally expensive to obtain ground truth information due to difficulties and costs associated with sample collection and analysis. In this paper, we present two Gaussian processes based approaches for improving the accuracy of vegetation parameter retrieval when ground truth is limited. The first is the adoption of covariance functions based on well-established metrics, such as, spectral angle and spectral correlation, which are known to be better measures of similarity for spectral data owing to their resilience to spectral variabilities. The second is the joint modeling of related vegetation parameters by multitask Gaussian processes so that the prediction accuracy of the vegetation parameter of interest can be improved with the aid of related vegetation parameters for which a larger set of ground truth is available. We experimentally demonstrate the efficacy of the proposed methods against existing approaches on three real-world hyperspectral datasets and one synthetic dataset. View Full-Text
Keywords: Gaussian processes; covariance functions; multitask learning; vegetation parameters; hyperspectral imaging Gaussian processes; covariance functions; multitask learning; vegetation parameters; hyperspectral imaging
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Gewali, U.B.; Monteiro, S.T.; Saber, E. Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth. Remote Sens. 2019, 11, 1614.

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