PARAMETER TRADE-OFFS FOR IMAGING SPECTROSCOPY SYSTEMS

With the advent of the EOS era and configurable sensors capable of gathering ever more detailed data, users of these instruments are faced with the twin problems of specifying data acquisition parameters and extracting desired information from the voluminous data. To help better understand these problems, reseach has been focused on understanding the remote sensing process as a system and investigating the interrelated effects of various parameters. An application of a system model is made to explore system parameter trade-offs for a model sensor based on HIRIS. Radiometric performance was studied, along with the effect on classification accuracy of several system parameters. Using a model scene based on typical agricultural reflectance and atmospheric conditions, the atmosphere and sensor are seen to have significant effects on the mean received signal and noise performance. The effect of random uncorrelated errors in the radiometric calibration of the detector array is seen to degrade system performance, especially in the spectral bands below 1-mu-m. Accurate pixel-to-pixel relative radiometric calibration and the use of the Image Motion Compensation (IMC) option are seen to improve classification accuracy. The IMC is seen to be especially helpful under low signal-level situations such as high solar zenith angles. The selection of feature sets based on combining spectral bands was studied under a variety of observational conditions. Feature sets chosen from characteristics of the scene performed best overall, but ones chosen based on signal-to-noise ratios were seen to be more robust.