Paper
13 March 2003 Extraction of visible SWIR hyperspectral scene statistics and their use in scene realization
Author Affiliations +
Proceedings Volume 4885, Image and Signal Processing for Remote Sensing VIII; (2003) https://doi.org/10.1117/12.463101
Event: International Symposium on Remote Sensing, 2002, Crete, Greece
Abstract
A method for the extraction of spectral and spatial scene statistics from hyperspectral data is discussed. The method is designed to work on atmospherically compensated data in any spectral region, although this paper will report on visible scene statistics derived from atmospherically compensated AVIRIS data. Our approach is based on a physical description where the scene is composed of materials that in turn are described by a set of spectral endmembers. The spatial statistics of individual scene materials have more stationary behavior than the statistics for the whole scene. For this reason we have formulated our approach around statistics that are determined from the fractional abundance images obtained from the spectral un-mixing of the scene. These quantities are used to construct a high spatial resolution reflectance or emissivity/temperature surface using a fast autoregressive texture generation tool. The spectral complexity of the synthetic surfaces have been evaluated by inserting objects for detection and calculating ROC curves. Preliminary results indicate that synthetic scenes with realistic levels of spectral clutter can be generated using spectral and spatial statistics determined from endmember fractional abundance maps. This work is motivated by the need for realistic hyperspectral scene generation capabilities to test future hyperspectral sensor concepts.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert L. Sundberg, John H. Gruninger, and Raymond Haren "Extraction of visible SWIR hyperspectral scene statistics and their use in scene realization", Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003); https://doi.org/10.1117/12.463101
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KEYWORDS
Atmospheric modeling

Reflectivity

Target detection

Data modeling

Monte Carlo methods

Algorithm development

Atmospheric particles

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