Quality and automation metrics for spectral classification algorithms

Erich D. Hernandez-Baquero; Robert V. Opalecky

doi:10.1117/12.451657

8 November 2002 Quality and automation metrics for spectral classification algorithms

Erich D. Hernandez-Baquero, Robert V. Opalecky

Proceedings Volume 4816, Imaging Spectrometry VIII; (2002) https://doi.org/10.1117/12.451657
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

Quantitative and robust metrics are required to objectively compare the performance of algorithms within a general functional class. This is especially true for classification algorithms that cluster and label data using spectral features. This is because spectral algorithms are usually based on a finite set of assumptions about the radiative transfer phenomenology. Thus, a suite of algorithms is needed to achieve a generalized and robust processing chain that performs well under all operational scenarios of interest. An adaptive processing chain that automatically selects the optimal combination of algorithms to generate a product of prescribed quality provides a framework for operational applications. To this end, we have developed Measures of Effectiveness (MOE's) and Figures of Merit (FOM's) that can quantitatively and objectively select the appropriate algorithm automatically. The FOM's are a weighted sum of MOE's, which are performance metrics such as the tightness and dissimilarity of clusters. We have also defined scene and sensor parameters that quantify a subset of factors that affect algorithm performance. Functional relationships between FOM's and MOE's and between the FOM's/MOE's and the scene/sensor parameters were also established. These functional relationships allow users to predict the expected classification product quality given a specific operational scenario based on a performance model that also automates the processing chain. Initial results of an application of this approach to hyperspectral data indicate that FOM's can be predicted with high accuracy with choices made correctly as high as 89% of the time depending on the FOM definitions. The results were obtained over a wide range of operational scenarios.

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Erich D. Hernandez-Baquero and Robert V. Opalecky "Quality and automation metrics for spectral classification algorithms", Proc. SPIE 4816, Imaging Spectrometry VIII, (8 November 2002); https://doi.org/10.1117/12.451657

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KEYWORDS

Sensors

Signal to noise ratio

Data modeling

Calibration

Atmospheric corrections

Evolutionary algorithms

Roads

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