Calibrated Vehicle Paint Signatures for Simulating Hyperspectral Imagery

Zachary Mulhollan, Aneesh Rangnekar, Timothy Bauch, Matthew J. Hoffman, Anthony Vodacek; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 110-111


We investigate a procedure for rapidly adding calibrated vehicle visible-near infrared (VNIR) paint signatures to an existing hyperspectral simulator - The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model - to create more diversity in simulated urban scenes. The DIRSIG model can produce synthetic hyperspectral imagery with user-specified geometry, atmospheric conditions, and ground target spectra. To render an object pixel's spectral signature, DIRSIG uses a large database of reflectance curves for the corresponding object material and a bidirectional reflectance model to introduce s due to orientation and surface structure. However, this database contains only a few spectral curves for vehicle paints and generates new paint signatures by combining these curves internally. In this paper we demonstrate a method to rapidly generate multiple paint spectra, flying a drone carrying a pushbroom hyperspectral camera to image a university parking lot. We then process the images to convert them from the digital count space to spectral reflectance without the need of calibration panels in the scene, and port the paint signatures into DIRSIG for successful integration into the newly rendered sets of synthetic VNIR hyperspectral scenes.

Related Material

author = {Mulhollan, Zachary and Rangnekar, Aneesh and Bauch, Timothy and Hoffman, Matthew J. and Vodacek, Anthony},
title = {Calibrated Vehicle Paint Signatures for Simulating Hyperspectral Imagery},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}