Hybrid pattern recognition method using evolutionary computing techniques applied to the exploitation of hyperspectral imagery and medical spectral data

Jerry A. Burman

doi:10.1117/12.373240

14 December 1999 Hybrid pattern recognition method using evolutionary computing techniques applied to the exploitation of hyperspectral imagery and medical spectral data

Jerry A. Burman

Author Affiliations +

Proceedings Volume 3871, Image and Signal Processing for Remote Sensing V; (1999) https://doi.org/10.1117/12.373240
Event: Remote Sensing, 1999, Florence, Italy

Abstract

Hyperspectral image sets are three dimensional data volumes that are difficult to exploit by manual means because they are comprised of multiple bands of image data that are not easily visualized or assessed. GTE Government Systems Corporation has developed a system that utilizes Evolutionary Computing techniques to automatically identify materials in terrain 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 and identifying pertinent materials that comprise a specific object of interest in the terrain and estimating the percentage of materials present within a pixel of interest (spectral unmixing). The method has been applied and evaluated against real hyperspectral imagery data from the AVIRIS sensor. In addition, the process has been applied to remotely sensed infrared spectra collected at the microscopic level to assess the amounts of DNA, RNA and protein present in human tissue samples as an aid to the early detection of cancer.

Citation Download Citation

Jerry A. Burman "Hybrid pattern recognition method using evolutionary computing techniques applied to the exploitation of hyperspectral imagery and medical spectral data", Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999); https://doi.org/10.1117/12.373240

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available