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Information Mining in Remote Sensing Image Archives

M. Datcu, K. Seidel and G. Schwarz
Machine Vision and Advanced Image Processing in Remote Sensing (MAVIRIC)
1999

Abstract

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 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 (ìdata miningî) 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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@InProceedings{eth_biwi_00187,
  author = {M. Datcu and K. Seidel and G. Schwarz},
  title = {Information Mining in Remote Sensing Image Archives},
  booktitle = {Machine Vision and Advanced Image Processing in Remote Sensing (MAVIRIC)},
  year = {1999},
  pages = {199--212},
  editor = {I. Kanellopoulos and G. Wilkinson and T. Moons},
  publisher = {Springer},
  keywords = {remote sensing, image indexing, database}
}