Forecasting and Analysis of Spectroradiometric System Performance (FASSP)
FASSP is a statistical model for the analysis of earth observing optical spectral imaging
systems and their performance. The basic concept is that land cover classes in a scene
(including objects that may be spatially unresolved) can be represented by a small number of
statistical parameters and these parameters can be propagated through the effects of
illumination, the intervening atmosphere, the sensor, and any processing algorithms to
arrive at an estimate of performance. The predominant surface parameters considered are
the spectral reflectance mean vector and covariance matrix. The performance is estimated in
the context of the specific application being considered. For example, in a target detection
case, the performance is the probability of detection at a given false alarm rate. In an
unmixing application, the performance is the mean and standard deviation of the abundance
of the various endmembers.
More information can be found on FASSP in the following publications describing its theory
and operation. The model was initially developed at MIT Lincoln Laboratory and
they retain the copyright.
J.P. Kerekes and J.E. Baum, "Full Spectrum Spectral Imaging System Analytical Model," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 5, pp. 571-580, March 2005. PDF
J.P. Kerekes and J.E. Baum, "Spectral Imaging System Analytical Model for Subpixel Object Detection," IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 5, pp. 1088-1101, May 2002. PDF
J.P. Kerekes and D.A. Landgrebe, "An Analytical Model of Earth-OBservational Remote Sensing Systems," IEEE Transactions on Systems, Man and Cybernetics, vol. 21, no. 1, pp. 125-133, January/February 1991. PDF
J. Kerekes and J. Baum, "Full spectrum modeling of at-sensor spectral radiance variability due to surface variability," Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage, Alaska, 20-24 September 2004.
J. Kerekes and D. Manolakis, "Improved modeling of background distributions in an end-to-end spectral imaging system model," Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage, Alaska, 20-24 September 2004.
J. Kerekes, "Spectral Imaging System Performance Forecasting," Proceedings of the IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, Greenbelt, Maryland, 27-28 October 2003.
J. Kerekes, M. Glennon, R. Lockwood, "Unmixing Analysis: Model Prediction Compared to Observed Results,” Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, 21-25 July 2003.
J. Kerekes, K. Farrar, N. Keshava, "Linear Unmixing Performance Forecasting,” Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Toronto, Canada, 24-28 June 2002.
J. Kerekes, M. Griffin, J. Baum, K. Farrar, “Modeling of LWIR Hyperspectral System Performance for Surface Object and Effluent Detection Applications,” Proceedings of Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, SPIE Vol. 4381, AeroSense , 16-20 April 2001.
J.P. Kerekes, J.E. Baum, and K.E. Farrar, “Analytical Model of Hyperspectral System Performance,” Proceedings of Infrared Imaging Systems: Design, Modeling, and Testing X, SPIE Vol. 3701, AeroSense, Orlando, FL, 5-9 April 1999.
J.P. Kerekes, "Parameter Impacts on Hyperspectral Remote Sensing System Performance," Hyperspectral Remote Sensing and Applications, SPIE Vol. 2821, pp. 195-201, Denver CO, August 5-6, 1996.
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Last updated: 1 November 2005