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

emulation interpolation machine learning scene simulation spectroscopy

Journal

Remote sensing
ISSN: 2072-4292
Titre abrégé: Remote Sens (Basel)
Pays: Switzerland
ID NLM: 101624426

Informations de publication

Date de publication:
16 Jan 2019
Historique:
entrez: 9 9 2022
pubmed: 16 1 2019
medline: 16 1 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 36082067
doi: 10.3390/rs11020157
pmc: PMC7613354
mid: EMS152627
doi:

Types de publication

Journal Article

Langues

eng

Pagination

157

Subventions

Organisme : European Research Council
ID : 755617
Pays : International

Déclaration de conflit d'intérêts

Conflicts of Interest: The authors declare no conflict of interest.

Références

Sensors (Basel). 2016 Aug 17;16(8):
pubmed: 27548168
Sensors (Basel). 2018 Sep 19;18(9):
pubmed: 30235885

Auteurs

Jochem Verrelst (J)

Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, Spain.

Juan Pablo Rivera Caicedo (JP)

Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, Spain.
CONACyT-UAN, Secretaría de Investigación y Posgrado, Universidad Autónoma de Nayarit, Ciudad de la Cultura Amado Nervo, Tepic CP. 63155, Nayarit, Mexico.

Jorge Vicent (J)

Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, Spain.

Pablo Morcillo Pallarés (P)

Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, Spain.

José Moreno (J)

Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, Spain.

Classifications MeSH