A number of organizations are using the data collected by the HYperspectral Digital Imagery Collection Experiment (HYDICE) airborne sensor to demonstrate the utility of hyperspectral imagery (HSI) for a variety of applications. The interpretation and extrapolation of these results can be influenced by the nature and magnitude of any artifacts introduced by the HYDICE sensor. A short study was undertaken which first reviewed the literature for discussions of the sensor' s noise characteristics and then extended those results with additional analyses of HYDICE data. These investigations used unprocessed image data from the onboard Flight Calibration Unit (FCU) lamp and ground scenes taken at three different sensor altitudes and sample integration times. Empirical estimates of the sensor signal-to-noise ratio (SNR) were compared to predictions from a radiometric performance model. The spectral band-to-band correlation structure of the sensor noise was studied. Using an end-to-end system performance model, the impact of various noise sources on subpixel detection was analyzed. The results show that, although a number of sensor artifacts exist, they have little impact on the interpretations of HSI utility derived from analyses of HYDICE data.

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. CAPT WEST JASON E FUNDING NUMBERS PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)ROCHESTER INSTITUTE OF TECHNOLOGY PERFORMING ORGANIZATION REPORT NUMBERCI04-1012 SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) ABSTRACTAlgorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters can be used to locate spectral targets by modeling scene background as either structured (geometric) with a set of endmembers (basis vectors) or as unstructured (stochastic) with a covariance or correlation matrix. These matrices are often calculated using all available pixels in a data set. In unstructured background research, various techniques for improving over the scene-wide method have been developed, each involving either the removal of target signatures from the background model or the segmenting of image data into spatial or spectral subsets. Each of these methods increase the detection signal to background ratio (SBR) and the multivariäte normality (MVN) of the data from which background statistics are calculated, thus increasing separation between target and non-target species in the matched filter detection statistic and ultimately improving thresholded target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This paper provides a review and comparison of methods in target exclusion, spatial subsetting, and spectral pre-clustering using preliminary matched filter detection results from a larger study. The analysis provides insight into the merit of employing unstructured background characterization techniques, as well as the limitations for their practical application.

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.

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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.

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As the field of remote imaging spectrometry grows, interest increases in applications that require the data be "corrected" to surface reflectance. However, the utility of the data for these applications will be limited by the accuracy to which the correction can be performed. Two approaches to atmospheric compensation are reviewed and applied to airbome spectrometer data to study their error characteristics. An end-to-end analysis model is used to extend the study to examine the effects of individual sources of error.Results indicate that random errors of 1 to 2% reflectance units and bias errors of 1 to 4% are achievable in atmospheric window regions, with considerably higher errors in atmospheric absorption bands.

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