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.
 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