Comprehensive hyperspectral system simulation: II. Hyperspectral sensor simulation and premliminary VNIR testing results

Richard J. Bartell; Craig R. Schwartz; Michael T. Eismann; Jack N. Cederquist; James A. Nunez; Lee Curtis Sanders; Alan H. Ratcliff; Barry W. Lyons; Stephen D. Ingle

doi:10.1117/12.410380

23 August 2000 Comprehensive hyperspectral system simulation: II. Hyperspectral sensor simulation and premliminary VNIR testing results

Richard J. Bartell, Craig R. Schwartz, Michael T. Eismann, Jack N. Cederquist, James A. Nunez, Lee Curtis Sanders, Alan H. Ratcliff, Barry W. Lyons, Stephen D. Ingle

Author Affiliations +

Proceedings Volume 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI; (2000) https://doi.org/10.1117/12.410380
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

An end-to-end hyperspectral system model with applications to space and airborne sensor platforms is under development and testing. In this paper we discuss current work in the development of the sensor model and the results of preliminary testing. It is capable of simulating collected hyperspectral imagery of the ground as sensors operating from space or airborne platforms would acquire it. Dispersive hyperspectral imaging sensors operating from the visible through the thermal infrared spectral regions can be modeled with actual hyperspectral imagery or simulated hyperspectral scenes used as inputs. In the sensor model portion, fore-optics (misalignment), dispersive spectrometer designs, degradations (platform motion, smile, keystone, misregistration), focal plane array (temperature drift, nonuniformity/nonlinearity), noise (shot, dark, Johnson, 1/f, RMS read, excess low frequency), analog-to-digital conversion, digital processing, and radiometric/temporal/wavelength calibration effects are included. The overall model includes a variety of processing algorithms including constant false alarm rate anomaly detection, spectral clustering of backgrounds for anomaly detection, atmospheric compensation, and pairwise adaptive linear matching for detection and classification. Results of preliminary testing using synthetic scene data in the visible/near infrared portion of the spectrum are discussed. Potential applications for this modeling capability include processing results performance prediction and sensor parameter specification trade studies.

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Richard J. Bartell, Craig R. Schwartz, Michael T. Eismann, Jack N. Cederquist, James A. Nunez, Lee Curtis Sanders, Alan H. Ratcliff, Barry W. Lyons, and Stephen D. Ingle "Comprehensive hyperspectral system simulation: II. Hyperspectral sensor simulation and premliminary VNIR testing results", Proc. SPIE 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, (23 August 2000); https://doi.org/10.1117/12.410380

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