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| Titel |
Automatic extraction of faults and fractal analysis from remote sensing data |
| VerfasserIn |
R. Gloaguen, P. R. Marpu, I. Niemeyer |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 14, no. 2 ; Nr. 14, no. 2 (2007-03-22), S.131-138 |
| Datensatznummer |
250012158
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| Publikation (Nr.) |
 copernicus.org/npg-14-131-2007.pdf |
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| Zusammenfassung |
| Object-based classification is a promising technique for image
classification. Unlike pixel-based methods, which only use the measured
radiometric values, the object-based techniques can also use shape and
context information of scene textures. These extra degrees of freedom
provided by the objects allow the automatic identification of geological
structures. In this article, we present an evaluation of object-based
classification in the context of extraction of geological faults. Digital
elevation models and radar data of an area near Lake Magadi (Kenya) have been
processed. We then determine the statistics of the fault populations. The
fractal dimensions of fault dimensions are similar to fractal dimensions
directly measured on remote sensing images of the study area using power
spectra (PSD) and variograms. These methods allow unbiased statistics of
faults and help us to understand the evolution of the fault systems in
extensional domains. Furthermore, the direct analysis of image texture is a
good indicator of the fault statistics and allows us to classify the
intensity and type of deformation. We propose that extensional fault networks
can be modeled by iterative function system (IFS). |
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