8 November 2002 Technique for automation of hyperspectral algorithm selection and performance prediction
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Abstract
The performance of hyperspectral exploitation algorithms depends on the quality of the hyperspectral data processed. Some algorithms may perform better or be better suited to certain types or quality of data than other algorithms. To improve the hyperspectral exploitation production process, the dependencies of different types of algorithms to the quality of the hyperspectral data needs to be understood. A framework for predicting algorithm performance based on data parameters and metrics is presented. Figures of merit are defined for classes of algorithms which can be used to select between different algorithms to process a particular dataset. A training set of data is used to determine the dependence of each algorithm being tested in the class. Multiple regression is then applied to determine the dependence of the algorithm results on the different parameters and metrics. The performance on datasets not in the training set can then be predicted using the results of the regression analysis. Analysis of the regression results provides insight into the dependence of different types of algorithms on parameters of the data. In addition, the results provide insight into the data quality needed to provide quality exploitation products that meet minimum requirements. The technique is presented along with preliminary results for some basic algorithms in the atmospheric compensation and material identification categories.
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Alan T. Buswell, Alan T. Buswell, Peter T. Telek, Peter T. Telek, Carina Dalton-Sorrell, Carina Dalton-Sorrell, Gary A. Petrick, Gary A. Petrick, } "Technique for automation of hyperspectral algorithm selection and performance prediction", Proc. SPIE 4816, Imaging Spectrometry VIII, (8 November 2002); doi: 10.1117/12.451648; https://doi.org/10.1117/12.451648
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