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The Cyborg Astrobiologist: matching of prior textures by image compression for geological mapping and novelty detection

Published online by Cambridge University Press:  19 February 2014

P.C. McGuire ,
A. Bonnici ,
K.R. Bruner ,
C. Gross ,
J. Ormö ,
R.A. Smosna ,
S. Walter  and
L. Wendt
P.C. McGuire*
Affiliation:
Planetary Sciences and Remote Sensing Group, Institute of Geological Sciences, Freie Universität Berlin, Berlin, Germany Formerly at: Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA
A. Bonnici
Affiliation:
Department of Systems and Control Engineering, University of Malta, Malta
K.R. Bruner
Affiliation:
Department of Geology and Geography, West Virginia University, Morgantown, WV, USA
C. Gross
Affiliation:
Planetary Sciences and Remote Sensing Group, Institute of Geological Sciences, Freie Universität Berlin, Berlin, Germany
J. Ormö
Affiliation:
Centro de Astrobiología, CSIC-INTA, Torrejón de Ardoz, Madrid, Spain
R.A. Smosna
Affiliation:
Department of Geology and Geography, West Virginia University, Morgantown, WV, USA
S. Walter
Affiliation:
Planetary Sciences and Remote Sensing Group, Institute of Geological Sciences, Freie Universität Berlin, Berlin, Germany
L. Wendt
Affiliation:
Planetary Sciences and Remote Sensing Group, Institute of Geological Sciences, Freie Universität Berlin, Berlin, Germany
*
e-mail: mcguirepatr@gmail.com
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Abstract

We describe an image-comparison technique of Heidemann and Ritter (2008a, b), which uses image compression, and is capable of: (i) detecting novel textures in a series of images, as well as of: (ii) alerting the user to the similarity of a new image to a previously observed texture. This image-comparison technique has been implemented and tested using our Astrobiology Phone-cam system, which employs Bluetooth communication to send images to a local laptop server in the field for the image-compression analysis. We tested the system in a field site displaying a heterogeneous suite of sandstones, limestones, mudstones and coal beds. Some of the rocks are partly covered with lichen. The image-matching procedure of this system performed very well with data obtained through our field test, grouping all images of yellow lichens together and grouping all images of a coal bed together, and giving 91% accuracy for similarity detection. Such similarity detection could be employed to make maps of different geological units. The novelty-detection performance of our system was also rather good (64% accuracy). Such novelty detection may become valuable in searching for new geological units, which could be of astrobiological interest. The current system is not directly intended for mapping and novelty detection of a second field site based on image-compression analysis of an image database from a first field site, although our current system could be further developed towards this end. Furthermore, the image-comparison technique is an unsupervised technique that is not capable of directly classifying an image as containing a particular geological feature; labelling of such geological features is done post facto by human geologists associated with this study, for the purpose of analysing the system's performance. By providing more advanced capabilities for similarity detection and novelty detection, this image-compression technique could be useful in giving more scientific autonomy to robotic planetary rovers, and in assisting human astronauts in their geological exploration and assessment.

Keywords

computer visionnovelty detectionsimilarity matchingphone-camerasimage compressionfield geologylichenscoal
Type
Research Article
Information
International Journal of Astrobiology , Volume 13 , Issue 3 , July 2014 , pp. 191 - 202
Copyright
Copyright © Cambridge University Press 2014 

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