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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: [email protected]
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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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References

Bartolo, A., McGuire, P.C., Camilleri, K.P., Spiteri, C., Borg, J.C., Farrugia, P.J., Ormö, J., Gomez-Elvira, J., Rodriguez-Manfredi, J.A., Diaz-Martinez, E., Ritter, H., Haschke, R., Oesker, M., Ontrup, J. (2007). The Cyborg Astrobiologist: porting from a wearable computer to the Astrobiology Phone-cam. Int. J. Astrobiol. 6, 255–261.Google Scholar
Bonnici, A., Gross, C., McGuire, P.C., Ormö, J., Walter, S. & Wendt, L. (2010). The Cyborg Astrobiologist: compressing images for the matching of prior textures and for the detection of novel textures. In European Planetary Science Congress (EPSC), vol. 5. Potsdam, Germany, extended abstract #162.Google Scholar
Castano, R., Estlin, T., Gaines, D., Chouinard, C., Bomstein, B., Anderson, R.C., Burl, M., Thompson, D., Castano, A. & Judd, M. (2007). Onboard autonomous rover science. In IEEE Aerospace Conf. Big Sky, Montana.Google Scholar
Fink, W., Dohm, J.M., Tarbell, M.A., Hare, T.M. & Baker, V.R. (2005). Next-generation robotic planetary reconnaissance missions: a paradigm shift. Planet. Space Sci. 53, 1419–1426.CrossRefGoogle Scholar
Gross, C., Wendt, L., McGuire, P.C., Bonnici, A., Foing, B.H., Souza-Egipsy, V., Bose, R., Walter, S., Ormö, J., Diaz-Martinez, E. (2009). Testing the Cyborg Astrobiologist at the Mars Desert Research Station (MDRS), Utah. In European Planetary Science Congress (EPSC), vol. 4. Potsdam, Germany, extended abstract #548.Google Scholar
Gross, C., Wendt, L., McGuire, P.C., Bonnici, A., Foing, B.H., Souza-Egipsy, V., Bose, R., Walter, S., Ormö, J., Díaz-Martínez, E., Oesker, M., Ontrup, J., Haschke, R., Ritter, H. (2010). The Cyborg Astrobiologist: testing a novelty detection algorithm at the Mars Desert Research Station (MDRS), Utah. In LPSCXLI, Lunar and Planetary Science Conf. Houston, Texas, extended abstract #2457.Google Scholar
Gulick, V.C., Morris, R.L., Ruzon, M.A. & Roush, T.L. (2001). Autonomous image analyses during the 1999 Marsokhod rover field test. J. Geophys. Res. 106, 7745–7764.Google Scholar
Gulick, V.C., Hart, S.D., Shi, X. & Siegel, V.L. (2004). Developing an automated science analysis system for Mars surface exploration for MSL and beyond. In Lunar and Planetary Institute Conf. XXXV, extended abstract #2121.Google Scholar
Halatci, I, Brooks, C.A. & Iagnemma, K. (2007). Terrain classification and classifier fusion for planetary exploration rovers. In IEEE Aerospace Conf., pp. 1–11. Big Sky, MT.Google Scholar
Halatci, I., Brooks, C.A. & Iagnemma, K. (2008). A study of visual and tactile terrain classification and classifier fusion for planetary exploration rovers. Robotica 26, 767–779.Google Scholar
Haralick, R.M., Shanmugam, K. & Dinstein, I.H. (1973). Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621.Google Scholar
Heidemann, G. & Ritter, H. (2008a). Compression for visual pattern recognition. In Proc. Third Int. Symp. Communications, Control and Signal Processing (ISCCSP 2008), pp. 1520–1523. IEEE, St. Julians, Malta.Google Scholar
Heidemann, G. & Ritter, H. (2008b). On the contribution of compression to visual pattern recognition. In Proc. Third Int'l Conf. on Comp. Vision Theory and Applications, Funchal, Madeira-Portugal, vol. 2, pp. 83–89.Google Scholar
Huffman, D.A. (1952). A method for the construction of minimum-redundancy codes. In Proc. Institute of Radio Engineers (IRE), pp. 1098–1102.Google Scholar
Kärkkäinen, J., Kempa, D. and Puglisi, S.J. (2013). Lightweight Lempel–Ziv parsing. In Proceedings of the 12th International Symposium, SEA 2013, Rome, Italy, June 5–7, 2013, Experimental Algorithms, ed. Bonifaci, V., Demetrescu, C., Marchetti-Spaccamela, A., pp. 139–150.Springer, Berlin, Heidelberg; http://arxiv.org/abs/1302.1064Google Scholar
Lempel, A. & Ziv, J. (1977). A universal algorithm for sequential data compression. IEEE Trans. Inf. Theory 23, 337–343.Google Scholar
McGuire, P.C., Ormö, J.O., Diaz, Martinez E., Rodriguez, Manfredi J.A., Gomez, Elvira J., Ritter, H., Oesker, M. & Ontrup, J. (2004). The Cyborg Astrobiologist: first field experience. Int. J. Astrobiol. 3, 189–207.CrossRefGoogle Scholar
McGuire, P.C., Diaz, Martinez E., Ormö, J.O., Gomez, Elvira J., Rodriguez, Manfredi J.A., Sebastian, Martinez E., Ritter, H., Haschke, R., Oesker, M. & Ontrup, J. (2005a). The Cyborg Astrobiologist: scouting red beds for uncommon features with geological significance. Int. J. Astrobiol. 4, 101–113.Google Scholar
McGuire, P.C., Gomez, Elvira J., Rodriguez, Manfredi J.A., Sebastian, Martinez E., Ormö, J., Diaz, Martinez E., Ritter, H., Oesker, M., Haschke, R. & Ontrup, J. (2005b). Field geology with a wearable computer: first results of the Cyborg Astrobiologist system. In Proc. ICINCO'2005 (Int. Conf. Informatics in Control, Automation and Robotics), vol. 3, pp. 283–291. 14–17 September, 2005, Barcelona, Spain.Google Scholar
McGuire, P.C., Wolff, M.J., Smith, M.D., Arvidson, R.E., Murchie, S.L., Clancy, R.T., Roush, T.L., Cull, S.C., Lichtenberg, K.A., Wiseman, S.M., Green, R.O., Martin, T.Z., Milliken, R.E., Cavender, P.J., Humm, D.C., Seelos, F.P., Seelos, K.D., Taylor, H.W., Ehlmann, B.L., Mustard, J.F., Pelkey, S.M., Titus, T.N., Hash, C.D., Malaret, E.R. (2008). MRO/CRISM retrieval of surface lambert albedos for multispectral mapping of Mars with DISORT-based radiative transfer modeling: Phase 1 – using historical climatology for temperatures, aerosol optical depths, and atmospheric pressures. Trans. Geosci. Remote Sens., 46, 4020–4040.Google Scholar
McGuire, P.C., Gross, C., Wendt, L., Bonnici, A., Souza-Egipsy, V., Ormö, J., Diaz-Martinez, E., Foing, B.H., Bose, R., Walter, S., Oesker, M., Ontrup, J., Haschke, R., Ritter, H. (2010). The Cyborg Astrobiologist: testing a novelty-detection algorithm on two mobile exploration systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah. Int. J. Astrobiol. 9, 11–27.Google Scholar
Rao, A.R. (2012). A Taxonomy for Texture Description and Identification. Springer Publishing Company, Incorporated.Google Scholar
Saravanan, C. & Ponalagusamy, R. (2010). Lossless grey-scale image compression using source symbols reduction and Huffman coding. Int. J. Image Process. (IJIP) 3, 246–251.Google Scholar
Thompson, D.R., Allwood, A., Bekker, D., Cabrol, N.A., Estlin, T., Fuchs, T. & Wagstaff, K.L. (2012). TextureCam: autonomous image analysis for astrobiology survey. In LPSCXLIII Lunar and Planetary Institute Conf., Houston, Texas, extended abstract #1659.Google Scholar
Volpe, R. (2003). Rover functional autonomy development for the Mars Mobile Science Laboratory. In IEEE Aerospace Conf., vol. 2, pp. 2_643–2_652. Big Sky, Montana.Google Scholar
Wendt, L., Gross, C., McGuire, P.C., Bonnici, A., Foing, B.H., Souza-Egipsy, V., Bose, R., Walter, S., Ormö, J., Díaz-Martínez, E., Oesker, M., Ontrup, J., Haschke, R., Ritter, H. (2009). The Cyborg Astrobiologist: teaching computers to find uncommon or novel areas of geological scenery in real-time. In European Space Agency Int. Conf. Comparative Planetology: Venus – Earth – Mars, ESTEC, Noordwijk, The Netherlands.Google Scholar