Improvements to an Earth observing statistical performance model with applications to LWIR spectral variability

The goal of this work is to examine emissivity signature variability (including particle size effects from the signature at the ground level (e.g., powers) into a detection context for study. We seek to answer how such variability impacts the measurement / detection problem. A model currently used at RIT, the Forecasting and Analysis of Spectroradiometric System Performance (FASSP) model, can handle most of the statistical model variations in the LWIR. However, this model only examines variations in radiance in the LWIR. For the visible portion of the EM spectrum, atmospheric compensation is performed so as to examine surface reflectance. What is missing is a temperature-emissivity separation (TES) routine such that transformations are performed on the statistics generated from such an algorithm. Furthermore, the model only uses a linear detection scheme such as a matched filter. The adaptive cosine estimate (ACE) should be implemented as a state-of-the-art detection scheme. This entails a non-linear Monte-Carlo-type approach for the solution.

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We have successfully derived and implemented the transformation of statistics associated with the iterative spectrally smooth temperature/ emissivity separation (ISSTES) algorithm. We have also implemented an equivalent ACE detector, called the cotangent detector, into FASSP using a Monte Carlo technique. Next we plan to extend this model to examine particle size distributions and implement a parameter trade-off study using FASSP.