No abstract

Visible to near-, shortwave-, and longwave-infrared (VNIR, SWIR, LWIR) hyperspectral data were integrated using a variety of approaches to take advantage of complementary wavelength-specific spectral characteristics for improved material classification. The first approach applied separate minimum noise fraction (MNF) transforms to the three regions and combined only non-noise transformed bands. A second approach integrated the VNIR, SWIR, and LWIR data before using MNF analysis to isolate linear band combinations containing high signal to noise. Spectral endmembers extracted from each integrated dataset were unmixed and spatially mapped using a partial unmixing approach. Integrated results were compared to baseline analyses of the separate spectral regions. Outcomes show that analyzing across the full VNIR-SWIR-LWIR spectrum improves material characterization and identification.

In the spectral science community, numerous spectral signatures are stored in databases representative of many sample materials collected from a variety of spectrometers and spectroscopists. Due to the variety and variability of the spectra that comprise many spectral databases, it is necessary to establish a metric for validating the quality of spectral signatures. This has been an area of great discussion and debate in the spectral science community. This paper discusses a method that independently validates two different aspects of a spectral signature to arrive at a final qualitative assessment; the textual meta-data and numerical spectral data. Results associated with the spectral data stored in the Signature Database 1 (SigDB) are proposed. The numerical data comprising a sample material's spectrum is validated based on statistical properties derived from an ideal population set. The quality of the test spectrum is ranked based on a spectral angle mapper (SAM) comparison to the mean spectrum derived from the population set. Additionally, the contextual data of a test spectrum is qualitatively analyzed using lexical analysis text mining. This technique analyzes to understand the syntax of the meta-data to provide local learning patterns and trends within the spectral data, indicative of the test spectrum's quality. Text mining applications have successfully been implemented for security 2 (text encryption/decryption), biomedical 3 , and marketing 4 applications. The text mining lexical analysis algorithm is trained on the meta-data patterns of a subset of high and low quality spectra, in order to have a model to apply to the entire SigDB data set. The statistical and textual methods combine to assess the quality of a test spectrum existing in a database without the need of an expert user. This method has been compared to other validation methods accepted by the spectral science community, and has provided promising results when a baseline spectral signature is present for comparison. The spectral validation method proposed is described from a practical application and analytical perspective. *

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. *