In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model is being developed. The model uses statistical descriptions of class reflectances in a scene and propagates them through the effects of the atmosphere, the sensor, and any processing transformations. A resultant system performance metric is then calculated based on these propagated statistics. The model divides a remote sensing system into three main components: the scene, the sensor, and the processing algorithms. Scene effects modeled include the solar illumination, atmospheric transmittance, shade effects, adjacency effects, and overcast clouds. Sensor effects modeled include the following radiometric noise sources: shot noise, thermal noise, detector readout noise, quantization noise, and relative calibration error. The processing component includes atmospheric compensation, various linear transformations, and a spectral matched filter used to obtain detection probabilities. This model has been developed for the HYDICE airborne imaging spectrometer covering the reflective solar spectral region from 0.4 to 2.5 rim. The paper presents the theory and operation of the model, as well as provides the results of validation studies comparing the model predictions to results obtained using HYDICE data. An example parameter trade study is also included to show the utility ofthe model for system design and operation applications.

Date of creation, presentation, or exhibit



Proceedings of Infrared Imaging Systems: Design, Modeling, and Testing X 3701 (1999) 155-166 "Analytical model of hyperspectral system performance," Proceedings of Infrared Imaging Systems: Design, Modeling, and Testing X. International Society of Optical Engineers. Held at AeroSense in Orlando, Florida: 5-9 April 1999. Copyright 1999 Society of Photo-Optical Instrumentation Engineers. This paper was published in Proceedings of Infrared Imaging Systems: Design, Modeling, and Testing X, vol.3701 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. This work was sponsored by the Department of Defense under Air Force contract F19628-95-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and not necessarily endorsed by the United States Air Force. The authors would like to acknowledge the support of the HYMSMO Program, and in particular Dr. Gregory Pavlin, the HYMSMO Technical Coordinator, for his support and interest in initiating this work. We would like to thank Mr. Larry Biehl, Purdue University, for his efforts in providing numerous spectral reflectance statistics files used in FASSP. Also, we would like to acknowledge Mr. Robert Basedow, Raytheon Optical Systems, Inc., for supplying detailed information to model the HYDICE sensor. ISSN: 0277-786X Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Conference Proceeding

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


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