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

: Kuny, Silvia; Hammer, Horst; Schulz, Karsten

Volltext ()

Paparoditis, N. ; International Society for Photogrammetry and Remote Sensing -ISPRS-:
XXIV ISPRS Congress "Imaging today, foreseeing tomorrow", Commission I : 5-9 July 2021, online, Nice, postponed to June 2022
Istanbul: ISPRS, 2021 (ISPRS Archives XLIII-B1-2021)
International Society for Photogrammetry and Remote Sensing (ISPRS Congress) <24, 2021, Nice/cancelled>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
SAR simulation; debris; damage detection; texture features; classifier performance

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.