Physically-based Thermal Simulation of Large Scenes for Infrared Imaging

Rendering large scenes in the thermal infrared spectrum requires the knowledge of the surface temperature distribution. We developed a workflow starting from raw airborne sensor data yielding to a physically-based thermal simulation, which can be used for rendering in the infrared spectrum. The workflow consists of four steps: material classification, mesh generation, material parameter assignment, and thermal simulation. This paper concerns the heat transfer simulation of large scenes. Our thermal model includes the heat transfer types radiation, convection, and conduction in three dimensions within the object and with its environment, i.e. sun and sky in particular. We show that our model can be solved by finite volume method and it shows good agreement with experimental data of the CUBI object. We demonstrate our workflow for sensor data from the City of Melville and produce reasonable results compared to infrared sensor data. For the large scene, the temperature simulation finished in appropriate time of 252 sec. for five day-night cycles.

[1]  Jean-Pierre Lagouarde,et al.  Modelling Daytime Thermal Infrared Directional Anisotropy over Toulouse City Centre , 2010 .

[2]  Lucien Wald,et al.  Outdoor Scene Synthesis in the Infrared Range for Remote Sensing Applications , 2002 .

[3]  J. Lienhard A heat transfer textbook , 1981 .

[4]  Dimitri Bulatov,et al.  Sensor data fusion for textured reconstruction and virtual representation of alpine scenes , 2017, Remote Sensing.

[5]  James T. Kajiya,et al.  The rendering equation , 1998 .

[6]  B. Bartos,et al.  FTOM-2D: a two-dimensional approach to model the detailed thermal behavior of nonplanar surfaces , 2015, SPIE Security + Defence.

[7]  Philippe Decaudin,et al.  Rendering Forest Scenes in Real-Time , 2010 .

[8]  Ashraf M. Dewan,et al.  Impact of Land Use and Land Cover Changes on Urban Land Surface Temperature , 2014 .

[9]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[10]  M. Pinar Mengüç,et al.  Thermal Radiation Heat Transfer , 2020 .

[11]  Eric Galin,et al.  Heat Transfer Simulation for Modeling Realistic Winter Sceneries , 2010, Comput. Graph. Forum.

[12]  Hermann Gross,et al.  EXTRACTION OF LINES FROM LASER POINT CLOUDS , 2006 .

[13]  Peter Grossmann,et al.  CUBI: a test body for thermal object model validation , 2007, SPIE Defense + Commercial Sensing.

[14]  D. Sonntag Important new values of the physical constants of 1986, vapour pressure formulations based on the ITS-90, and psychrometer formulae , 1990 .

[15]  R. Pajarola Overview of Quadtree-based Terrain Triangulation and Visualization , 2002 .

[16]  Dimitri Bulatov,et al.  Context-based automatic reconstruction and texturing of 3D urban terrain for quick-response tasks , 2014 .

[17]  Changming Sun,et al.  Semi-automated infrared simulation on real urban scenes based on multi-view images. , 2016, Optics express.

[18]  Filip Biljecki,et al.  An improved LOD specification for 3D building models , 2016, Comput. Environ. Urban Syst..

[19]  S. J. Oude Elberink,et al.  Building modeling from noisy photogrammetric point clouds , 2014 .

[20]  David Belton,et al.  Classification and representation of commonly used roofing material using multisensorial aerial data , 2018 .

[21]  S. Myint,et al.  Exploring the effect of neighboring land cover pattern on land surface temperature of central building objects , 2016 .