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Proceedings Paper

Extraction of spatial and spectral scene statistics for hyperspectral scene simulation
Author(s): Rosemary Kennett; Robert L. Sundberg; John Gruninger; Raymond Haren
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Paper Abstract

A method for extracting statistics from hyperspectral data and generating synthetic scenes suitable for scene generation models is presented. Regions composed of a general surface type with a small intrinsic variation, such as a forest or crop field, are selected. The spectra are decomposed using a basis set derived from spectra present in the scene and the abundances of the basis members in each pixel spectrum found. Statistics such as the abundance means, covariances and channel variances are extracted. The scenes are synthesized using a coloring transform with the abundance covariance matrix. The pixel-to-pixel spatial correlations are modeled by an autoregressive moving average texture generation technique. Synthetic reflectance cubes are constructed using the generated abundance maps, the basis set and the channel variances. Enhancements include removing any pattern from the scene and reducing the skewness. This technique is designed to work on atmospherically-compensated data in any spectral region, including the visible-shortwave infrared HYDICE and AVIRIS data presented here. Methods to evaluate the performance of this approach for generating scene textures include comparing the statistics of the synthetic surfaces and the original data, using a signal-to-clutter ratio metric, and inserting sub-pixel spectral signatures into scenes for detection using spectral matched filters.

Paper Details

Date Published: 29 September 2006
PDF: 12 pages
Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 63650X (29 September 2006); doi: 10.1117/12.705026
Show Author Affiliations
Rosemary Kennett, Spectral Sciences, Inc. (United States)
Robert L. Sundberg, Spectral Sciences, Inc. (United States)
John Gruninger, Spectral Sciences, Inc. (United States)
Raymond Haren, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 6365:
Image and Signal Processing for Remote Sensing XII
Lorenzo Bruzzone, Editor(s)

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