No abstract

There is a growing number of infrared (IR) spectral signature data in the scientific community gathered from a variety of sensors using a variety of collection techniques. As the quantity of collected data grows, automated solutions for searching and matching signatures need to be developed. When searching and matching signatures, reducing computational complexity and increasing matching accuracy are essential. We present a signature classification method via k-means clustering using a novel application of spectral angle mapping to efficiently determine spectral similarity. We evaluate the method against spectral data in the "SigDB" spectral analysis software application developed by the Johns Hopkins University Applied Physics Laboratory (JHU/APL). The key component to this approach is the set of characteristic functions used to map signatures' similarity into a spatial representation. Existing methods used to autonomously identify and classify IR spectral data include spectral angle mapping and key feature detection. Spectral mapping is computationally slow due to the need for direct individual comparison, and key feature detection improves computation time but is limited by the specific features selected for comparison. The accuracy and computation time of the spectral cluster classification method is evaluated against spectral angle mapping and visual analyses on the ASTER NASA spectral library. The goal of this method is to improve both the accuracy and speed of classifying newly collected unlabeled spectra. We find that the proposed method of scoring signatures offers a speed increase of three orders of magnitude in comparing spectra at the expense of a high false positive rate, suitable for use as a first-pass filter. We further find that the k-means cluster-based classification is highly sensitive to the selection of initial cluster centroids, and offer alternative solutions to use with our scoring method.

In the area of collecting field spectral data using a spectrometer, it is common to have the instrument over the material of interest. In certain instances it is beneficial to have the ability to remotely control the spectrometer. While several systems have the ability to use a form of connectivity to capture the measurement it is essential to have the ability to control the settings. Additionally, capturing reference information (metadata) about the setup, system configuration, collection, location, atmospheric conditions, and sample information is necessary for future analysis leading towards material discrimination and identification. This has the potential to lead to cumbersome field collection and a lack of necessary information for post processing and analysis. The method presented in this paper describes a capability to merge all parts of spectral collection from logging reference information to initial analysis as well as importing information into a web-hosted spectral database. This allows the simplification of collecting, processing, analyzing and storing field spectra for future analysis and comparisons. This concept is developed for field collection of thermal data using the Designs and Prototypes (D&P) Hand Portable FT-IR Spectrometer (Model 102). The remote control of the spectrometer is done with a customized Android application allowing the ability to capture reference information, process the collected data from radiance to emissivity using a temperature emissivity separation algorithm and store the data into a custom web-based service. The presented system of systems allows field collected spectra to be used for various applications by spectral analysts in the future. *