A Caltech Library Service

Automated Global Feature Analyzer - A Driver for Tier-Scalable Reconnaissance

Fink, Wolfgang and Datta, Ankur and Dohm, James M. and Tarbell, Mark A. and Jobling, Farrah M. and Furfaro, Roberto and Kargel, Jeffrey S. and Schulze-Makuch, Dirk and Baker, Victor R. (2008) Automated Global Feature Analyzer - A Driver for Tier-Scalable Reconnaissance. In: Aerospace Conference, 2008 IEEE : 1-8 March 2008 : [Big Sky, Montana]. IEEE Aerospace Conference Proceedings. IEEE , Piscataway, NJ, pp. 1841-1852. ISBN 978-1-4244-1487-1.

PDF - Published Version
See Usage Policy.


Use this Persistent URL to link to this item:


For the purposes of space flight, reconnaissance field geologists have trained to become astronauts. However, the initial forays to Mars and other planetary bodies have been done by purely robotic craft. Therefore, training and equipping a robotic craft with the sensory and cognitive capabilities of a field geologist to form a science craft is a necessary prerequisite. Numerous steps are necessary in order for a science craft to be able to map, analyze, and characterize a geologic field site, as well as effectively formulate working hypotheses. We report on the continued development of the integrated software system AGFA: automated global feature analyzerreg, originated by Fink at Caltech and his collaborators in 2001. AGFA is an automatic and feature-driven target characterization system that operates in an imaged operational area, such as a geologic field site on a remote planetary surface. AGFA performs automated target identification and detection through segmentation, providing for feature extraction, classification, and prioritization within mapped or imaged operational areas at different length scales and resolutions, depending on the vantage point (e.g., spaceborne, airborne, or ground). AGFA extracts features such as target size, color, albedo, vesicularity, and angularity. Based on the extracted features, AGFA summarizes the mapped operational area numerically and flags targets of "interest", i.e., targets that exhibit sufficient anomaly within the feature space. AGFA enables automated science analysis aboard robotic spacecraft, and, embedded in tier-scalable reconnaissance mission architectures, is a driver of future intelligent and autonomous robotic planetary exploration.

Item Type:Book Section
Related URLs:
Additional Information:© 2008 IEEE.
Series Name:IEEE Aerospace Conference Proceedings
Record Number:CaltechAUTHORS:20100708-082154493
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:18942
Deposited By: Tony Diaz
Deposited On:09 Jul 2010 17:22
Last Modified:08 Nov 2021 23:48

Repository Staff Only: item control page