Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation
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 e...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/11/2/157 |
| _version_ | 1798030961626054656 |
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| author | Jochem Verrelst Juan Pablo Rivera Caicedo Jorge Vicent Pablo Morcillo Pallarés José Moreno |
| author_facet | Jochem Verrelst Juan Pablo Rivera Caicedo Jorge Vicent Pablo Morcillo Pallarés José Moreno |
| author_sort | Jochem Verrelst |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T19:48:33Z |
| format | Article |
| id | doaj.art-d8237f752d9b46888af851ba671b9439 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T19:48:33Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d8237f752d9b46888af851ba671b94392022-12-22T04:06:22ZengMDPI AGRemote Sensing2072-42922019-01-0111215710.3390/rs11020157rs11020157Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene GenerationJochem Verrelst0Juan Pablo Rivera Caicedo1Jorge Vicent2Pablo Morcillo Pallarés3José Moreno4Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, SpainImage Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, SpainImage Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, SpainImage Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, SpainImage Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, SpainCollection 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.http://www.mdpi.com/2072-4292/11/2/157emulationmachine learninginterpolationspectroscopyscene simulation |
| spellingShingle | Jochem Verrelst Juan Pablo Rivera Caicedo Jorge Vicent Pablo Morcillo Pallarés José Moreno Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation Remote Sensing emulation machine learning interpolation spectroscopy scene simulation |
| title | Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation |
| title_full | Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation |
| title_fullStr | Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation |
| title_full_unstemmed | Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation |
| title_short | Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation |
| title_sort | approximating empirical surface reflectance data through emulation opportunities for synthetic scene generation |
| topic | emulation machine learning interpolation spectroscopy scene simulation |
| url | http://www.mdpi.com/2072-4292/11/2/157 |
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