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A pattern recognition system for locating small volvanoes in Magellan SAR images of VenusThe Magellan data set constitutes an example of the large volumes of data that today's instruments can collect, providing more detail of Venus than was previously available from Pioneer Venus, Venera 15/16, or ground-based radar observations put together. However, data analysis technology has not kept pace with data collection and storage technology. Due to the sheer size of the data, complete and comprehensive scientific analysis of such large volumes of image data is no longer feasible without the use of computational aids. Our progress towards developing a pattern recognition system for aiding in the detection and cataloging of small-scale natural features in large collections of images is reported. Combining classical image processing, machine learning, and a graphical user interface, the detection of the 'small-shield' volcanoes (less than 15km in diameter) that constitute the most abundant visible geologic feature in the more that 30,000 synthetic aperture radar (SAR) images of the surface of Venus are initially targeted. Our eventual goal is to provide a general, trainable tool for locating small-scale features where scientists specify what to look for simply by providing examples and attributes of interest to measure. This contrasts with the traditional approach of developing problem specific programs for detecting Specific patterns. The approach and initial results in the specific context of locating small volcanoes is reported. It is estimated, based on extrapolating from previous studies and knowledge of the underlying geologic processes, that there should be on the order of 10(exp 5) to 10(exp 6) of these volcanoes visible in the Magellan data. Identifying and studying these volcanoes is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is forbiddingly time-consuming. Hence, the development of techniques to partially automate this task were undertaken. The primary constraints for this particular problem are that the method must be reasonably robust and fast. Unlike most geological features, the small volcanoes of Venus can be ascribed to a basic process that produces features with a short list of readily defined characteristics differing significantly from other surface features on Venus. For pattern recognition purposes the relevant criteria include (1) a circular planimetric outline, (2) known diameter frequency distribution from preliminary studies, (3) a limited number of basic morphological shapes, and (4) the common occurrence of a single, circular summit pit at the center of the edifice.
Document ID
Document Type
Conference Paper
Burl, M. C. (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Fayyad, U. M. (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Smyth, P. (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Aubele, J. C. (Brown Univ. Providence, RI., United States)
Crumpler, L. S. (Brown Univ. Providence, RI., United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1993
Publication Information
Publication: Lunar and Planetary Inst., Twenty-fourth Lunar and Planetary Science Conference. Part 1: A-F
Subject Category
Distribution Limits
Work of the US Gov. Public Use Permitted.

Related Records

IDRelationTitle19940007055Analytic PrimarySixth Annual Workshop on Space Operations Applications and Research (SOAR 1992), volume 219940007543Analytic PrimaryTwenty-fourth Lunar and Planetary Science Conference. Part 1: A-F