Categorization of hyperspectral information (HSI) based on the distribution of spectra in hyperspace

Ronald G. Resmini

doi:10.1117/12.486731

23 September 2003 Categorization of hyperspectral information (HSI) based on the distribution of spectra in hyperspace

Ronald G. Resmini

Proceedings Volume 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX; (2003) https://doi.org/10.1117/12.486731
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

Hyperspectral information (HSI) data are commonly categorized by a description of the dominant physical geographic background captured in the image cube. In other words, HSI categorization is commonly based on a cursory, visual assessment of whether the data are of desert, forest, urban, littoral, jungle, alpine, etc., terrains. Additionally, often the design of HSI collection experiments is based on the acquisition of data of the various backgrounds or of objects of interest within the various terrain types. These data are for assessing and quantifying algorithm performance as well as for algorithm development activities. Here, results of an investigation into the validity of the backgrounds-driven mode of characterizing the diversity of hyperspectral data are presented. HSI data are described quantitatively, in the space where most algorithms operate: n-dimensional (n-D) hyperspace, where n is the number of bands in an HSI data cube. Nineteen metrics designed to probe hyperspace are applied to 14 HYDICE HSI data cubes that represent nine different backgrounds. Each of the 14 sets (one for each HYDICE cube) of 19 metric values was analyzed for clustering. With the present set of data and metrics, there is no clear, unambiguous break-out of metrics based on the nine different geographic backgrounds. The break-outs clump seemingly unrelated data types together; e.g., littoral and urban/residential. Most metrics are normally distributed and indicate no clustering; one metric is one outlier away from normal (i.e., two clusters); and five are comprised of two distributions (i.e., two clusters). Overall, there are three different break-outs that do not correspond to conventional background categories. Implications of these preliminary results are discussed as are recommendations for future work.

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Ronald G. Resmini "Categorization of hyperspectral information (HSI) based on the distribution of spectra in hyperspace", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); https://doi.org/10.1117/12.486731

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KEYWORDS

Picosecond phenomena

Statistical analysis

Visualization

Algorithm development

Data acquisition

Data modeling

Space operations

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