Automated Classification of Thermal Infrared Spectra Using Self-Organizing Maps.

Details Automated Classification of Thermal Infrared Spectra Using Self-Organizing Maps. Automated Classification of Thermal Infrared Spectra Using Self-Organizing Maps.

Sep 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006dps....38.4517r&link_type=abstract

American Astronomical Society, DPS meeting #38, #45.17; Bulletin of the American Astronomical Society, Vol. 38, p.570

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Existing and planned space missions to a variety of planetary and satellite surfaces produce an ever increasing volume of spectral data. Understanding the scientific informational content in this large data volume is a daunting task. Fortunately various statistical approaches are available to assess such data sets. Here we discuss an automated classification scheme based on Kohonen Self-Organizing maps (SOM) we have developed. The SOM process produces an output layer were spectra having similar properties lie in close proximity to each other. One major effort is partitioning this output layer into "appropriate” regions. This is preformed by defining closed regions based upon the strength of the boundaries between adjacent cells in the SOM output layer. We use the Davies-Bouldin index as a measure of the inter-class similarities and intra-class dissimilarities that determines the optimum partition of the output layer, and hence number of SOM clusters. This allows us to identify the natural number of clusters formed from the spectral data. Mineral spectral libraries prepared at Arizona State University (ASU) and John Hopkins University (JHU) are used to test and evaluate the classification scheme. We label the library sample spectra in a hierarchical scheme with class, subclass, and mineral group names. We use a portion of the spectra to train the SOM, i.e. produce the output layer, while the remaining spectra are used to test the SOM. The test spectra are presented to the SOM output layer and assigned membership to the appropriate cluster. We then evaluate these assignments to assess the scientific meaning and accuracy of the derived SOM classes as they relate to the labels. We demonstrate that unsupervised classification by SOMs can be a useful component in autonomous systems designed to identify mineral species from reflectance and emissivity spectra in the thermal IR.

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