Recreation of a nominal polarimetric scene using synthetic modeling tools

Abstract

Comparisons have been made showing that modeled multi and hyperspectral imagery can approach the complexity of real data and the use of modeled data to perform algorithm testing and sensor modeling is well established. With growing interest in the acquisition and exploitation of polarimetric imagery, there is a need to perform similar comparisons for this imaging modality. This paper will describe the efforts to reproduce polarimetric imagery acquired of a real world scene in a synthetic image generation environment. Real data was collected with the Wildfire Airborne Sensor ProgramLite (WASP-Lite) imaging system using three separate cameras to acquire simultaneously three polarization orientations. Modeled data were created using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. This model utilizes existing tools such as polarized bi-directional reflectance distribution functions (pBRDF), polarized atmospheric models, and polarization-sensitive sensor models to recreate polarized imagery. Results will show comparisons between the real and synthetic imagery, highlighting successes in the model as well as areas where improved fidelity is required.

Modeling and measurement of optical polarimetric image phenomenology in a complex urban environment

“…DIRSIG performs ray-tracing calculations of "light" emanating from realistic models of electromagnetic sources (e.g., sun, moon), reflecting off of virtual 3-D objects composed of various materials, and arriving at a virtual imaging detector with a specific array size and optical field of view. Coregistered detector "channels" can be specified to output [5]. Alternatively, channels can be designed to output spectral radiance that has passed through ideal linear polarizers oriented at several angles (e.g., 0 , 45 , 90 , & 135 ) to mirror the operation of an imaging polarimeter, and combined into Stokes vector imagery via equation 1 assuming negligible circular polarization (i.e., L ,3 0).…”

Sensor modeling and demonstration of a multi-object spectrometer for performance-driven sensing

“…Image products generated from such a sensor, such as Degree and Angle of Polarization, can add a new dimension of discriminability to the tracking algorithm by keying on such vehicle characteristics as surface roughness and orientation. A framework for simulating polarimetric data has been established for DIRSIG 17 , and the model has been shown to be able to accurately reproduce a nominal polarized scene 18 . However before such a data set can be generated, polarized BRDF (pBRDF) models must be attributed to both the static and dynamic scene content.…”