The quantitative forecasting of hyperspectral system performance is an important capability at every stage of system development including system requirement definition, system design, and sensor operation. In support of this, Lincoln Laboratory has been developing an analytical modeling tool to predict end-to-end spectroradiometric remote sensing system performance. Recently, the model has been extended to more accurately depict complex natural scenes by including multiple classes in the target pixel through the use of a linear mixing model. Additionally, a linear unmixing algorithm has been implemented to predict retrieved fractional abundances and their associated errors due to both natural variability and corrupting noise sources. This paper describes the details of this multiple target class model enhancement. Comparisons are presented between the model predictions and measured spectral radiances, as well as unmixing results obtained from data collected by NASA’s EO-1 Hyperion space-based hyperspectral sensor. Additionally, results of an analysis using the enhanced model are presented to show the sensitivity of end member fractional abundance estimates to system parameters using linear unmixing techniques.

Date of creation, presentation, or exhibit



Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 3 (2002) 1676-1678 "Linear unmixing performance forecasting," Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Institute of Electrical and Electronics Engineers (IEEE). Held in Toronto, Canada: 24-28 June 2002. ©2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. This work was sponsored by the Department of Defense under Contract F19628-00-C-0002. ISBN: 078-03-7536-XNote: 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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