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  4. On the classifier performance for simulation based debris detection in SAR imagery
 
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2021
Konferenzbeitrag
Titel

On the classifier performance for simulation based debris detection in SAR imagery

Abstract
Urban areas struck by disasters such as earthquakes are in need of a fast damage detection assessment. A post-event SAR image often is the first available image, most likely with no matching pre-event image to perform change detection. In previous work we have introduced a debris detection algorithm for this scenario that is trained exclusively with synthetically generated training data. A classification step is employed to separate debris from similar textures such as vegetation. In order to verify the use of a random forest classifier for this context, we conduct a performance comparison with two alternative popular classifiers, a support vector machine and a convolutional neural network. With the direct comparison revealing the random forest classifier to be best suited, the effective performance on the prospect of debris detection is investigated for the post-earthquake Christchurch scene. Results show a good separation of debris from vegetation and gravel, thus reducing the false alarm rate in the damage detection operation considerably.
Author(s)
Kuny, Silvia
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Hammer, Horst
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Schulz, Karsten
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Hauptwerk
XXIV ISPRS Congress "Imaging today, foreseeing tomorrow", Commission I
Konferenz
International Society for Photogrammetry and Remote Sensing (ISPRS Congress) 2021
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DOI
10.5194/isprs-archives-XLIII-B1-2021-45-2021
Externer Link
Externer Link
Language
Englisch
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IOSB
Tags
  • SAR simulation

  • debris

  • damage detection

  • texture features

  • classifier performanc...

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