Data from multispectral and hyperspectral imaging systems have been used in many applications including land cover classification, surface characterization, material identification, and spatially unresolved object detection. While these optical spectral imaging systems have provided useful data, their design and utility could be further enhanced by better understanding the sensitivities and relative roles of various system attributes; in particular, when application data product accuracy is used as a metric. To study system parameters in the context of land cover classification, an end-to-end remote sensing system modeling approach was previously developed. In this paper, we extend this model to subpixel object detection applications by including a linear mixing model for an unresolved object in a background and using object detection algorithms and probability of detection (PD) versus false alarm (PFA) curves to characterize performance. Validations with results obtained from airborne hyperspectral data show good agreement between model predictions and the measured data. Examples are presented which show the utility of the modeling approach in understanding the relative importance of various system parameters and the sensitivity of PD versus PFA curves to changes in the system for a subpixel road detection scenario (Refer to PDF file for exact formulas).

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©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. Dr. G. Pavlin is acknowledged for his role in providing an impetus for this work. CAPT. D. Martin and CAPT. F. Garcia, ODUSD (S&T), are acknowledged for their support under the Hyperspectral Technology Assessment Program (HTAP). Acknowledgment is also granted to the Spectral Information Technology Applications Center (SITAC) for providing the HYDICE data. K. Farrar of Lincoln Laboratory is gratefully acknowledged for her contributions in implementing the model and for conducting numerous analyses. Also, Dr. S. Hsu of Lincoln Laboratory is acknowledged for performing the empirical analysis described in Section III-C. The comments and suggestions of the anonymous reviewers are also much appreciated.ISSN:0196-2892 Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

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Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


RIT – Main Campus