Summary
Conventional satellite ground segments comprise mission operations and data acquisition systems, data ingestion interfaces, processing capabilities, a catalogue of available data, a data archive, and interfaces for queries and data dissemination. The transfer of data mostly relies on common computer networks or high capacity tapes. Future ground segments have to be capable of handling multi-mission data delivered by Synthetic Aperture Radar (SAR) and optical sensors and have to provide easy user access to support the selection of specific data sets, fuse data, visualize products and to compress data for transmission via Internet. In particular, the search for data sets has to support individual queries by data content and detailed application area (“data mining”) as well as capabilities for automated extraction of relevant features and the application oriented representation of results. In the case of SAR image data, we face an enormous volume of raw data combined with very specific analysis requests posed by the users. In order to reconcile these conflicting aspects we suggest the development of a tool for interactive generation of high level products. This tool shall extract, evaluate, and visualize the significant information from multidimensional image data of geographically localized areas. It shall rely on advanced image compression methods. Thus, the tool will support the monitoring of the Earth’s surface from various perspectives like vegetation, ice and snow, or ocean features. This exchange of information can be understood as image communication, i.e. the transfer of information via visual data. We propose an advanced remote sensing ground segment architecture designed for easier and interactive decision-making applications and a broader dissemination of remote sensing data. This article presents the concept developed at the German Aerospace Center, DLR Oberpfaffenhofen in collaboration with the Swiss Federal Institute of Technology, ETH Zurich, for information retrieval from remote sensing (RS) data. The research line has as very pragmatic goals the design of future RS ground segment systems permitting fast distribution and easy accessibility to the data, real and near real-time applications, and to promote the implementation of data distribution systems. However, the scope of the project is much larger, addressing basic problems of image and information representation, with a large potential of application in other fields: medical sciences, multimedia, interactive television, etc.
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References
“EEE Computer - Special Issue on Databases” September 1995.
“IEEE Transactions on Knowledge and Data Engineering - Special Issue on Data Mining” December 1996.
“IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Digital Lbraries” August 1996.
“IEEE Computer Graphics and Applications - Special Issue on Visualization” July/August 1997.
“Pattern Recognition - Special Issue on Image Databases” April 1997.
U. Benz, M. Datcu, G. Schwarz, and K. Seidel, “Image communication: New features of a remote sensing data ground segment” in CEOS SAR Workshop, ESTEC, February 1998.
Ch. A. Bouman and M. Shapiro, “A multiscale random field model for Bayesian image segmentation” IEEE Transactions on Image Processing, vol. 3, pp. 162–177, December 1994.
M. Datcu and F. Holecz, “Generation of synthetic images for radiometric topographic correction of optical imagery” in SPIE Proceedings on Recent Advances in Sensors, Radiometric Calibration and Processing of Remotely Sensed Data, Orlando, USA (P.S. Chavez and R.A. Schowengerdt, eds.), vol. SPIE-1938, pp. 260-271, SPIE OE/Aerospace Sensing, 1993.
M. Datcu, K. Seidel, and M. Walessa, “Spatial information retrieval from remote sensing images-Part I: Information theoretical perspective” IEEE Transactions on Geoscience and Remote Sensing, vol. 36, no. 5, pp. 1431–1445, September 1998.
DLR-DFD, “Intelligent satellite data information system” http://isis.dlr.de/, 1998
G. Healy and A. Jain, “Retrieval of multispectral satellite images using physicsbased invariant representations” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, pp. 842–848, 1996.
D.J.C. MacKay, Maximum Entropy and Bayesian Methods in Inverse Problems, ch. Bayesian Interpolation, pp. 39-66, Kluwer Academic Publisher, 1991.
J. Mao and A.K. Jain, “Texture classification and segmentation using multiresolution simultaneous autoregressive models” Pattern Recognition, vol. 25, no. 2, pp. 173–188, 1992.
A. O’agan, Bayesian Inference, Kendall’ Library of Statistics, 1994.
A. Pentland, R.W. Picard, and S. Sclaroff, “Photobook: Tools for contentbased manipulation of image databases” in Storage and Retrieval for Image and Video Databases II (W. Niblack and R.C. Jain, eds.), vol. SPIE-2185, pp. 34-47, SPIE, February 1994.
H. Rehrauer, K. Seidel, and M. Datcu, “ultiscale markov random fields for large image datasets representation” in Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS’7), A Scientific Vision for Sustainable Development (T.I. Stein, ed.), vol. I, pp. 255-257, 1997.
C.P. Robert, The Bayesian Choice, vol. Springer Texts in Statistics, Berlin: Springer-Verlag, 1996.
M. Schroder, H. Rehrauer, K. Seidel, and M. Datcu, “Spatial information retrieval from remote sensing images: Part B. Gibbs Markov random fields” IEEE Transactions on Geoscience and Remote Sensing, vol. 36, no. 5, pp. 1446–1455, September 1998.
M. Schroder, K. Seidel, and M. Datcu, “Gibbs random field models for image content characterization” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS’7) (T.I. Stein, ed.), vol. I, pp. 258-260, 1997.
M. Schroder, K. Seidel, and M. Datcu, “User-oriented content labelling in remote sensing image archives” in Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS’8), 6-10 July 1998, Seattle, WA, vol. II, pp. 1019-1021, 1998.
K. Seidel, R. Mastropietro, and M. Datcu, “New architectures for remote sensing image archives” in Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS’97) (T.I. Stein, ed.), vol. I, pp. 616-618, 1997.
K. Seidel, M. Schroder, H. Rehrauer, and M. Datcu, “Query by image content from remote sensing archives” in Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS’98), 6-10 July 1998, Seattle, WA, vol. I, pp. 393-396, 1998.
K. Seidel, J.-P. Therre, M. Datcu, H. Rehrauer, and M. Schroder, “Advanced query and retrieval techniques for remote sensing image databases (RSIA II and III)” Project Description Homepage, 1998, http://www.vision.ee.ethz.ch/~rsia/rsia2.html.
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Datcu, M., Seidel, K., Schwarz, G. (1999). Information Mining in Remote Sensing Image Archives. In: Kanellopoulos, I., Wilkinson, G.G., Moons, T. (eds) Machine Vision and Advanced Image Processing in Remote Sensing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60105-7_18
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