Intricate multiple scattering features of artificial facilities in X-Band SAR images
Abstract. Due to the intricate distortion and reflection geometry of the SAR signal, it is typically difficult to determine the multiple scattering of large artificial objects in SAR images. This work presents a scattering point path tracking model that utilizes the real three-dimensional dimensions of targets, based on the geometric optics method. Three different artificial structures, including light poles, cable stayed bridges, and power transmission lines, are carefully analysed in time-series SAR images with their simulated multiple scattering results. The results demonstrate that the routes determined by the model are consistent with the multiple scattering features on SAR images. Moreover, the time-series data demonstrate that ripples in the water's surface have a significant impact on the multi-scattering features of power lines and bridges. The double scattering features of the light pole provides a novel approach to the process of permanent scatterers (PS) in urban areas. The instances presented in this study demonstrate the effectiveness of the scattering point path tracking model in identifying the various artificial facility targets on different reflective surfaces. It will be a useful tool for deciphering the multiple scattering of large artificial structures when their 3D model is known.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Intricate multiple scattering features of artificial facilities in X-Band SAR images ; volume:X-1-2024 ; year:2024 ; pages:153-161 ; extent:9
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; X-1-2024 (2024), 153-161 (gesamt 9)
- Klassifikation
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Elektrotechnik, Elektronik
- Urheber
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Ma, Sijie
Li, Tao
Liu, Yan
Liu, Jie
- DOI
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10.5194/isprs-annals-X-1-2024-153-2024
- URN
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urn:nbn:de:101:1-2405160454434.461942817229
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:48 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Ma, Sijie
- Li, Tao
- Liu, Yan
- Liu, Jie
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