Identifying vehicles with VNIR-SWIR hyperspectral imagery:  sources of distinguishability and confusion

Steven Adler-Golden; Robert Sundberg

doi:10.1117/12.2238811

19 September 2016 Identifying vehicles with VNIR-SWIR hyperspectral imagery: sources of distinguishability and confusion

Steven Adler-Golden, Robert Sundberg

Author Affiliations +

Proceedings Volume 9976, Imaging Spectrometry XXI; 99760K (2016) https://doi.org/10.1117/12.2238811
Event: SPIE Optical Engineering + Applications, 2016, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

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 (19 September 2016); https://doi.org/10.1117/12.2238811

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