Paper Info
Title
Driving Realistic Texture in Simulated Long-Wave Infrared Imagery
Abstract
The visually-realistic yet radiometrically-accurate simulation of long-wave infrared (LWIR) imagery is a problem that has plagued members of industry and academia alike. The goal of imagery simulation is to provide a practical alternative to the often staggering effort required to collect actual data. Texture is a major factor that impacts both the visual appearance and the overall scene statistics of imagery throughout the electromagnetic spectrum. Lack of adequate texture often severely limits the use of synthetic scenes for realistic target detection algorithm development. Simulating texture in LWIR imagery is a much more complex task than simulating it in reflective-band imagery. This is because texture is not just a function of observable (in-band) surface properties (i.e. variation in reflectivity or emissivity) but also of temperature variation which is a function of solar absorptivity, surface orientation, shadowing and bulk thermal properties. To deal with these additional sources of variability, we have improved the Digital Imaging and Remote Sensing Image Generation (DIRSIG) (1) model to allow the use of texture maps to control various thermal properties. In addition, we have developed methods for constructing these thermal property texture maps based upon analysis of multi-temporal, Visible (VIS) and LWIR imagery. This paper presents these methods and applies them in order to simulate LWIR imagery taken over natural desert scenes in Trona, CA. These simulated LWIR images are the result of model-based fits of the DIRSIG thermodynamic model with varying thermal materials properties using the observed imagery. Methods to extend this approach will be discussed at the conclusion of this paper.
Year
DOI
Venue
2008
10.1109/IGARSS.2008.4779451
IGARSS
Keywords
Field
DocType
infrared,layout,remote sensing,azimuth,simulation,shadow mapping,texture mapping,frequency modulation,imaging,root mean squared error,materials,sunlight,pixel,thermodynamics,digital images,material properties,surface texture,spectrum,image texture,data mining,radiometry,modeling
Computer vision,Texture mapping,Thermal,Computer science,Image texture,Remote sensing,Azimuth,Mean squared error,Digital imaging,Pixel,Artificial intelligence,Infrared
Conference
Citations 
PageRank 
References 
2
0.71
0
Authors
5
Name
Order
Citations
PageRank
Jason T. Ward120.71
Stephen R. Lach221.38
John R. Schott37319.89
Niek J. Sanders420.71
Scott D Brown5206.89