Journal cover Journal topic
Geographica Helvetica
Journal topic

Journal metrics

CiteScore value: 1.8
CiteScore
1.8
SNIP value: 0.879
SNIP0.879
IPP value: 0.79
IPP0.79
SJR value: 0.404
SJR0.404
Scimago H <br class='widget-line-break'>index value: 17
Scimago H
index
17
h5-index value: 13
h5-index13
Volume 58, issue 2
Geogr. Helv., 58, 154–168, 2003
https://doi.org/10.5194/gh-58-154-2003
© Author(s) 2003. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geogr. Helv., 58, 154–168, 2003
https://doi.org/10.5194/gh-58-154-2003
© Author(s) 2003. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 Jun 2003

30 Jun 2003

Image information mining : exploration of Earth observation archives

M. Dateu1 and K. Seidel2 M. Dateu and K. Seidel
  • 1German Aerospace Center &ndahs; DLR, Remote Sensing Technology Institute – IMF, Oberpfaffenhofen, 82234 Wessling, Germany
  • 2Remote Sensing Group, Computer Vision Lab ETHZ, Gloriastrasse 35, 8092 Zürich, Switzerland

Abstract. The new generation of high resolution imaging satellites acquires huge amounts of data which are stored in large archives. The state-of-the-art Systems for data access allow only queries by geographical location, time of acquisition or type of sensor. This information is often less important than the content of the scene, i.e. structures, objects or scattering properties. Meanwhile, many new applications of remote sensing data are closer to Computer vision and require the knowledge of complicated spatial and structural relationships among image objects.

We are creating an intelligent satellite information mining system, a next generation architecture to help users to rapidly collect information, a tool to enhance and to manage the huge amount of historical and newly acquired satellite data-sets by giving experts access to relevant information in an understandable and directly usable form and to provide friendly interfaces for information query and browsing.

Research topics are within the frame of Bayesian learning, content-based querying, data modelling and adaptation to user conjecture.

Publications Copernicus
Download
Citation