Abstract-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 ( ) versus false alarm ( ) 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 versus curves to changes in the system for a subpixel road detection scenario.Index Terms-Hyperspectral imaging, multispectral imaging, remote sensing system modeling, subpixel object detection.

Abstract-In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model has been extended to cover the visible through longwave infrared portion of the optical spectrum (0.4-14 m). The model uses statistical descriptions of surface spectral reflectances/emissivities and temperature variations in a scene and propagates them through the effects of the atmosphere, the sensor, and processing transformations. A resultant system performance metric is then calculated based on these propagated statistics. This paper presents theory for the analytical transformation of surface statistics to at-sensor spectral radiance statistics for a downward-looking hyperspectral sensor observing both reflected sunlight and thermally emitted radiation. Comparisons of the model predictions with measurements from an airborne hyperspectral sensor are presented. Example parameter trades are included to show the utility of the model for applications in sensor design and operation.Index Terms-Full-spectrum modeling, hyperspectral imaging, midwave infrared (MWIR), multispectral imaging, remote sensing system modeling, thermal infrared.

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.