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31 October 1997 Nonliteral pattern recognition method for hyperspectral imagery exploitation using evolutionary computing methods
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Abstract
Multispectral and hyperspectral image sets contain large amounts of data which are difficult to exploit by manual means because they are comprised of multiple bands of image data that are not easily visualized or assessed.Non-literal imagery exploitation refers to a process that exploits non- spatial information by focusing on individual pixel signatures that span the spectral range of the sensor. GTE has developed a system that utilizes evolutionary computing techniques as a potential aid to imagery analysts to perform automatic object detection, recognition and materials identification on multispectral and hyperspectral imagery. The system employs sophisticated signature preprocessing and a unique combination of non-parametric search algorithms guided by a model based cost function to achieve rapid convergence and pattern recognition. The system is scaleable and is capable of discriminating decoys from real objects, identifying pertinent materials that comprise a specific object of interest and estimating the percentage of materials present within a pixel of interest.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jerry A. Burman "Nonliteral pattern recognition method for hyperspectral imagery exploitation using evolutionary computing methods", Proc. SPIE 3118, Imaging Spectrometry III, (31 October 1997); https://doi.org/10.1117/12.278935
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