A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest.
The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings.
In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness. This framework is composed of three modules (see figure):
- The central core, which consists of the aforementioned ECM;
- The synthetic-spectra generator, which, coupled with the ECM, generates a population of automatically retrieved spectral solutions; and
- The synthetic-spectra degeneracy analyzer (“degeneracy” is used here in the mathematical sense of signifying the existence of multiple equally valid solutions for a given set of data and user-defined accuracy), which applies several well-established mathematical methods to characterize the uniqueness (or the degeneracy, which is essentially the lack of uniqueness) of the solutions within the population.
One advantage afforded by this ECM-based methodology over traditional spectral retrieval methods is the ability to perform an automatic, unbiased search for all solutions within the entire parameter space, using criteria that make searching computationally far more economical than in complete-enumeration (“brute force”), Monte Carlo, or random searches. (As used here, “unbiased” characterizes a search that does not depend on initial ad hoc guesses by experts.) Other advantages include the following:
- Optimal solutions are found;
- Better interpretations of planetary spectral and angular data are possible, and initial tests have shown these interpretations to be consistent with ground truth; and
- The methodology is not limited to specific problems, and can be extended to solve problems of greater complexity.
This work was done by Richard Terrile, Wolfgang Fink, Terrance Huntsberger, Seungwon Lee, Edwin Tisdale, Paul Von Allmen, and Giovanna Tinetti of Caltech for NASA’s Jet Propulsion Laboratory.
In accordance with Public Law 96-517, the contractor has elected to retain title to this invention. Inquiries concerning rights for its commercial use should be addressed to:
Innovative Technology Assets Management
Mail Stop 202-233
4800 Oak Grove Drive
Pasadena, CA 91109-8099
Refer to NPO-42564, volume and number of this NASA Tech Briefs issue, and the page number.
This Brief includes a Technical Support Package (TSP).
Evolutionary Computing Methods for Spectral Retrieval
(reference NPO-42564) is currently available for download from the TSP library.
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