■ To support hyperspectral sensor system design and parameter trade-off investigations, Lincoln Laboratory has developed an analytical end-to-end model that forecasts remote sensing system performance. The model uses statistical descriptions of scene class reflectances and transforms them to account for the effects of the atmosphere, the sensor, and any processing operations. System-performance metrics can then be calculated on the basis of these transformed 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. Modeled sensor effects include radiometric noise sources, such as shot noise, thermal noise, detector readout noise, quantization noise, and relative calibration error. The processing component includes atmospheric compensation, various linear transformations, and a number of operators used to obtain detection probabilities. Models have been developed for several imaging spectrometers, including the airborne Hyperspectral Digital Imagery Collection Experiment (HYDICE) instrument, which covers the reflective solar spectral region from 0.4 to 2.5 μm. This article presents the theory and operation of the model, and provides example parameter trade studies to show the utility of the model for system design and sensor operation applications.

https://www.cis.rit.edu/people/faculty/kerekes/pdfs/LLJ_2003_Kerekes.pdf

Hyperspectral Imaging System Modeling

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 (P/sub D/) versus false alarm (P/sub FA/) 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 P/sub D/ versus P/sub FA/ curves to changes in the system for a subpixel road detection scenario.

Spectral imaging system analytical model for subpixel object detection

https://www.cis.rit.edu/people/faculty/kerekes/pdfs/TGRS_2002_Kerekes_Baum.pdf

Several types of noise in remote-sensing systems are treated. The purpose is to provide enhanced understanding of the relationship of noise sources to both analysis results and sensor design. The context of optical sensors and spectral pattern recognition analysis methods is used to enable tractability for quantitative results. First, the concept of multispectral classification is reviewed. Next, stochastic models are discussed for both signals and noise, including thermal, shot and quantization noise along with atmospheric effects. A model enabling the study of the combined effect of these sources is presented, and a system performance index is defined. Theoretical results showing the interrelated effects of the noise sources on system performance are given. Results of simulations using the system model are presented for several values of system parameters, using some noise parameters of the Thematic Mapper scanner as an illustration. Results show the relative importance of each of the noise sources on system performance, including how sensor noise interacts with atmospheric effects to degrade accuracy

Noise in Remote-Sensing Systems: The Effect on Classification Error

The authors present a system model for the remote sensing process and some results that yield insight into the process. Key results include interrelations between the atmosphere, sensor noise, sensor view angle and scattered path radiance and their influence on classification accuracy of the ground cover type. Also included are results indicating the tradeoffs in ground cell size and surface spatial correlation and their effect on classification accuracy. >

Stephen Jay Whitsitt

Papers

Error estimation and separability measures in feature selection for multiclass pattern recognition.