Validation of the background simulation model MATISSE: comparing results with MODIS satellite images

Schweitzer, Caroline; Stein, Karin; Wendelstein, Norbert; Labarre, Luc; Caillault, Karine; Fauqueux, Sandrine; Malherbe, Claire; Roblin, Antoine; Rosier, Bernard; Simoneau, Pierre

doi:10.1117/12.901099

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…As presented during the SPIE Remote Sensing Europe 2011 [3], very good comparison results have been received for the MWIR spectrum. The latest analysis have been carried out to investigate the performance of MATISSE code in those parts of the electromagnetic spectrum, were thermal effects are dominating (LWIR) on the one hand, and were reflection is the major contributor on the other hand (VIS).…”

Section: Terrestrial Backgroundmentioning

confidence: 90%

“…This chapter gives a brief summary of the applied context data in connection with the validation process. Detailed information has already been published under [3] and [4].…”

Section: Context Datamentioning

confidence: 99%

“…Both images are presented in Figure 1. For more details on the MODIS sensor please refer to [3]. The identification of cloud types on satellite images with only little context information is quite challenging.…”

Section: Modis Satellite Imagesmentioning

confidence: 99%

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Validation of MATISSE background images and cloud simulations: comparing results with MODIS satellite images

2013

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In this paper, we present the process of validating the atmospheric modelling software MATISSE (Advanced Modelling of the Earth for the Imaging and the Simulation of the Scenes and their Environment, developed by ONERA) by comparing simulation results of terrestrial and atmospheric background to MODIS satellite images. Analyses have been carried out for wavebands in the visible (VIS) as well as the longwave infrared (LWIR) spectrum.

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Section: Terrestrial Backgroundmentioning

confidence: 90%

“…This chapter gives a brief summary of the applied context data in connection with the validation process. Detailed information has already been published under [3] and [4].…”

Section: Context Datamentioning

confidence: 99%

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Validation of MATISSE background images and cloud simulations: comparing results with MODIS satellite images

2013

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“…Specialized modeling tools focused on this application area include FLiES [7], FLIGHT [8], and librat [9]. The set of more flexible models aimed at general-purpose remote sensing application areas include DART [10], MCScene [11], and MA-TISSE [12]. The DIRSIG model falls into the latter category of generic remote sensing simulation tools, with its particular strength being a sensor-driven approach that allows the end user to model the complexity of modern remote sensing systems in great detail.…”

Section: Introductionmentioning

confidence: 99%

DIRSIG5: Next-Generation Remote Sensing Data and Image Simulation Framework

Goodenough

Brown

2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The digital imaging and remote sensing image generation model is a physics-based image and data simulation model that is primarily used to generate synthetic imagery across the visible to thermal infrared regions using engineering-driven descriptions of remote sensing systems. The model recently went through a major redesign and reimplementation effort to address changes in user requirements and numerical computation trends that have emerged in the 15 years since the last major development effort. The new model architecture adopts some of the latest light transport algorithms matured by the computer graphics community and features a framework that is easily parallelized at the microscale (multithreading) and macroscale (cluster-based computing). A detailed description of the framework is provided, including a novel method for efficiently storing, evaluating, integrating, and sampling spherical and hemispherical datasets appropriate for the representation of modeled or measured bidirectional scattering, reflectance, and transmission distribution functions. The capabilities of the model are then briefly demonstrated and cross-verified with scenarios of interest to the remote sensing community.

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“…The French Aerospace lab ONERA has been collaborating with the Fraunhofer IOSB in the development of a radiative transfer model for few years. Next to maritime environment [20], MATISSE has already been validated for space-based observations [11] [21] as well as specific homogenous cloud models [16] [22]. As mentioned before, a need has been expressed for the use of heterogeneous clouds in the model.…”

Section: Introductionmentioning

confidence: 99%

Development of an atmospheric infrared radiation model with high clouds for target detection

et al. 2016

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In the field of target detection, the simulation of the camera FOV (field of view) background is a significant issue. The presence of heterogeneous clouds might have a strong impact on a target detection algorithm. In order to address this issue, we present here the construction of the CERAMIC package (Cloudy Environment for RAdiance and MIcrophysics Computation) that combines cloud microphysical computation and 3D radiance computation to produce a 3D atmospheric infrared radiance in attendance of clouds. The input of CERAMIC starts with an observer with a spatial position and a defined FOV (by the mean of a zenithal angle and an azimuthal angle). We introduce a 3D cloud generator provided by the French LaMP for statistical and simplified physics. The cloud generator is implemented with atmospheric profiles including heterogeneity factor for 3D fluctuations. CERAMIC also includes a cloud database from the French CNRM for a physical approach. We present here some statistics developed about the spatial and time evolution of the clouds. Molecular optical properties are provided by the model MATISSE (Modélisation Avancée de la Terre pour l'Imagerie et la Simulation des Scènes et de leur Environnement). The 3D radiance is computed with the model LUCI (for LUminance de CIrrus). It takes into account 3D microphysics with a resolution of 5 cm-1 over a SWIR bandwidth. In order to have a fast computation time, most of the radiance contributors are calculated with analytical expressions. The multiple scattering phenomena are more difficult to model. Here a discrete ordinate method with correlated-K precision to compute the average radiance is used. We add a 3D fluctuations model (based on a behavioral model) taking into account microphysics variations. In fine, the following parameters are calculated: transmission, thermal radiance, single scattering radiance, radiance observed through the cloud and multiple scattering radiance. Spatial images are produced, with a dimension of 10 km x 10 km and a resolution of 0.1 km with each contribution of the radiance separated. We present here the first results with examples of a typical scenarii. A 1D comparison in results is made with the use of the MATISSE model by separating each radiance calculated, in order to validate outputs. The code performance in 3D is shown by comparing LUCI to SHDOM model, referency code which uses the Spherical Harmonic Discrete Ordinate Method for 3D Atmospheric Radiative Transfer model. The results obtained by the different codes present a strong agreement and the sources of small differences are considered. An important gain in time is observed for LUCI versus SHDOM. We finally conclude on various scenarios for case analysis.

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Validation of the background simulation model MATISSE: comparing results with MODIS satellite images

Cited by 7 publications

References 13 publications

Validation of MATISSE background images and cloud simulations: comparing results with MODIS satellite images

Validation of MATISSE background images and cloud simulations: comparing results with MODIS satellite images

DIRSIG5: Next-Generation Remote Sensing Data and Image Simulation Framework

Development of an atmospheric infrared radiation model with high clouds for target detection

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