<title>Analytical model of hyperspectral system performance</title>

Abstract

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.

Behavioral model and simulator for the Multi-slit Optimized Spectrometer (MOS)

“…1,2 In addition to aiding engineering design and evaluation, simulators can also be a sandbox for testing new signal correction and product algorithms. 3,4 The Multi-Slit Optimized Spectrometer (MOS) is a NASA funded Instrument Incubator Program (IIP) to advance an innovative dispersive spectrometer concept in support of the GEO-CAPE ocean science mission.…”

<title>Modeling of LWIR hyperspectral system performance for surface object and effluent detection applications</title>

“…The sensor output statistics are then transformed through user-selectable processing functions to result in a performance metric such as spectral characterization accuracy, or a probability of detection vs. false alarm curve (P D /P FA ). Our previous work 3 described in detail the equations used to propagate the statistics to obtain a performance metric for the reflective solar part of the spectrum. …”

“…The at-sensor spectral radiance covariance matrix was derived by computing the expectation of the scene radiance defined in equation (1) with the mean subtracted times the transpose of that quantity as shown in equation (3).…”

“…Previously, an end-to-end reflective solar hyperspectral remote sensing system model had been developed using an analytical framework 3 . This model, known as Forecasting and Analysis of Spectroradiometric System Performance (FASSP), has been used for sensitivity studies and requirements analysis for systems operating in the VNIR/SWIR portion of the spectrum.…”

<title>Characterization of gaseous effluents from modeling of LWIR hyperspectral measurements</title>

“…The simulation of the scene radiance for plume cases was primarily used for the development of applications for FASSP 5 . It has proven a useful tool for the understanding of effects of plumes on the outgoing radiance field and provides a testbed for the ongoing effluent detection and identification study 12 .…”