From Event: SPIE Optical Engineering + Applications, 2016
Multispectral and hyperspectral imaging can facilitate vehicle tracking across a series of images by gathering spectral information that distinguishes the vehicle of interest from confusers. Developing effective algorithms for utilizing this information requires an understanding of the sources and nature of both the common and unique components in vehicle spectra, as well as the variations associated with lighting, view angle, and part of the vehicle being observed. In this study, focusing on the VNIR-SWIR spectral region, we analyze hyperspectral data from a recent field experiment at the Rochester Institute of Technology. We describe the spectra of painted vehicle surfaces in general terms, and demonstrate effective classification of automobiles based on spectra from upward facing surfaces (the roof, hood or trunk) using a method that combines the Support Vector Machine with data pre-conditioning.
Steven Adler-Golden and Robert Sundberg, "Identifying vehicles with VNIR-SWIR hyperspectral imagery: sources of distinguishability and confusion," Proc. SPIE 9976, Imaging Spectrometry XXI, 99760K (Presented at SPIE Optical Engineering + Applications: August 29, 2016; Published: 19 September 2016); https://doi.org/10.1117/12.2238811.
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