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Practical approach for synthetic aperture radar change analysis in urban environments

 
: Boldt, Markus; Thiele, Antje; Schulz, Karsten; Meyer, Franz J.; Hinz, Stefan

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Volltext urn:nbn:de:0011-n-5593687 (10 MByte PDF)
MD5 Fingerprint: 034338baef3c53af6061da58d42e14ba
Copyright Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 8.10.2019


Journal of applied remote sensing : JARS 13 (2019), Nr.3, Paper 034528, 30 S.
ISSN: 1931-3195
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IOSB ()
change analysis; synthetic aperture radar; TerraSAR-X; Open Access

Abstract
Change detection using remote sensing imagery is a broad and highly active field of research that has produced many different technical approaches for multiple applications. The majority of these approaches have in common that they do not deliver any detailed information concerning the type, category, or class of the detected changes. With respect to the extraction of such information, recent research often suggests that a land use classification is required. This classification can be accomplished in an unsupervised or supervised way, whereas the practicability of both strategies is more or less limited by the usage of reference or training data. Moreover, expert knowledge is needed to arrive at meaningful land use classes. An approach is presented that overcomes these drawbacks. A time series of synthetic aperture radar amplitude images is considered, enabling the detection of so-called high activity objects in urban environments. Such objects represent the basis of the investigations and denote the input for unsupervised categorization and classification procedures. The method supports even the unexperienced user in learning the actual information content leading to the capability to define a suitable scheme for change classification. Tests carried out on two different datasets suggest that the method is both practical and robust.

: http://publica.fraunhofer.de/dokumente/N-559368.html