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Article

Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation

1
Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, Spain
2
CONACyT-UAN, Secretaría de Investigación y Posgrado, Universidad Autónoma de Nayarit, Ciudad de la Cultura Amado Nervo, Tepic CP. 63155, Nayarit, Mexico
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(2), 157; https://doi.org/10.3390/rs11020157
Received: 30 November 2018 / Revised: 7 January 2019 / Accepted: 13 January 2019 / Published: 16 January 2019
(This article belongs to the Special Issue Recent Trends and Applications for Imaging Spectroscopy)
Collection of spectroradiometric measurements with associated biophysical variables is an essential part of the development and validation of optical remote sensing vegetation products. However, their quality can only be assessed in the subsequent analysis, and often there is a need for collecting extra data, e.g., to fill in gaps. To generate empirical-like surface reflectance data of vegetated surfaces, we propose to exploit emulation, i.e., reconstruction of spectral measurements through statistical learning. We evaluated emulation against classical interpolation methods using an empirical field dataset with associated hyperspectral spaceborne CHRIS and airborne HyMap reflectance spectra, to produce synthetic CHRIS and HyMap reflectance spectra for any combination of input biophysical variables. Results indicate that: (1) emulation produces surface reflectance data more accurately than interpolation when validating against a separate part of the field dataset; and (2) emulation produces the spectra multiple times (tens to hundreds) faster than interpolation. This technique opens various data processing opportunities, e.g., emulators not only allow rapidly producing large synthetic spectral datasets, but they can also speed up computationally intensive processing routines such as synthetic scene generation. To demonstrate this, emulators were run to simulate hyperspectral imagery based on input maps of a few biophysical variables coming from CHRIS, HyMap and Sentinel-2 (S2). The emulators produced spaceborne CHRIS-like and airborne HyMap-like surface reflectance imagery in the order of seconds, thereby approximating the spectra of vegetated surfaces sufficiently similar to the reference images. Similarly, it took a few minutes to produce a hyperspectral data cube with a spatial texture of S2 and a spectral resolution of HyMap. View Full-Text
Keywords: emulation; machine learning; interpolation; spectroscopy; scene simulation emulation; machine learning; interpolation; spectroscopy; scene simulation
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MDPI and ACS Style

Verrelst, J.; Rivera Caicedo, J.P.; Vicent, J.; Morcillo Pallarés, P.; Moreno, J. Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation. Remote Sens. 2019, 11, 157. https://doi.org/10.3390/rs11020157

AMA Style

Verrelst J, Rivera Caicedo JP, Vicent J, Morcillo Pallarés P, Moreno J. Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation. Remote Sensing. 2019; 11(2):157. https://doi.org/10.3390/rs11020157

Chicago/Turabian Style

Verrelst, Jochem, Juan P. Rivera Caicedo, Jorge Vicent, Pablo Morcillo Pallarés, and José Moreno. 2019. "Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation" Remote Sensing 11, no. 2: 157. https://doi.org/10.3390/rs11020157

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