Commenced in January 2007
Paper Count: 31108
Outdoor Anomaly Detection with a Spectroscopic Line Detector
Authors: O. J. G. Somsen
Abstract:One of the tasks of optical surveillance is to detect anomalies in large amounts of image data. However, if the size of the anomaly is very small, limited information is available to distinguish it from the surrounding environment. Spectral detection provides a useful source of additional information and may help to detect anomalies with a size of a few pixels or less. Unfortunately, spectral cameras are expensive because of the difficulty of separating two spatial in addition to one spectral dimension. We investigate the possibility of modifying a simple spectral line detector for outdoor detection. This may be especially useful if the area of interest forms a line, such as the horizon. We use a monochrome CCD that also enables detection into the near infrared. A simple camera is attached to the setup to determine which part of the environment is spectrally imaged. Our preliminary results indicate that sensitive detection of very small targets is indeed possible. Spectra could be taken from the various targets by averaging columns in the line image. By imaging a set of lines of various widths we found narrow lines that could not be seen in the color image but remained visible in the spectral line image. A simultaneous analysis of the entire spectra can produce better results than visual inspection of the line spectral image. We are presently developing calibration targets for spatial and spectral focusing and alignment with the spatial camera. This will present improved results and more use in outdoor application.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109485Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1335
 Dimitris Manolakis, David Marden, and Gary A. Shaw. 2003. Hyperspectral Image Processing for Automatic Target Detection Applications. Lincoln Laboratory Journal Volume 14, Number 1, 2003.
 Eismann, M. T., Stocker, A. D., & Nasrabadi, N. M. (2009). Automated hyperspectral cueing for civilian search and rescue. Proceedings of the IEEE, 97(6), 1031-1055.
 Chang, C. I., & Hsueh, M. (2006). Characterization of anomaly detection in hyperspectral imagery. Sensor Review, 26(2), 137-146.
 Smetek, T. E., & Bauer, K. W. (2008). A comparison of multivariate outlier detection methods for finding hyperspectral anomalies. Military Operations Research, 13(4), 19-43.
 Yuliya Tarabalka, Trym Vegard Haavardsholm, Ingebjørg Ka˚sen, Torbjørn Skauli. 2009. Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing. Journal of Real-Time Image Processing 4, 3 (2009) 287-300.
 Marshall, T., & Perkins, L. N. (2015). Color outlier detection for search and rescue. Technical Report No. ECE-2015-01. Department of Electrical and Computer Engineering. Boston University