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

Kuny, S.; Hammer, H.; Schulz, K.

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.

Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1682-1750
KITopen-ID: 1000140392
Erschienen in Volume XLIII-B1-2021, XXIV ISPRS Congress Imaging today, foreseeing tomorrow, Commission I: 2021 edition, 5–9 July 2021. Ed.: N. Paparoditis
Veranstaltung 24th ISPRS Congress (2021), Online, 05.07.2021 – 09.07.2021
Verlag International Society for Photogrammetry and Remote Sensing (ISPRS)
Seiten 45-50
Serie The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 43
Schlagwörter SAR simulation, debris, damage detection, texture features, classifier performance
Nachgewiesen in Scopus
Dimensions
Globale Ziele für nachhaltige Entwicklung Ziel 11 – Nachhaltige Städte und Gemeinden

Verlagsausgabe §
DOI: 10.5445/IR/1000140392
Veröffentlicht am 26.11.2021
Originalveröffentlichung
DOI: 10.5194/isprs-archives-XLIII-B1-2021-45-2021
Seitenaufrufe: 66
seit 28.11.2021
Downloads: 79
seit 28.11.2021
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