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Comparison of using single- or multi-polarimetric TerraSAR-X images for segmentation and classification of man-made maritime objects

: Teutsch, Michael; Saur, Günter

Preprint urn:nbn:de:0011-n-1927753 (3.0 MByte PDF)
MD5 Fingerprint: 374dd9e48ae4fa48f3c986aa74cb4308
Copyright 2011 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: 4.2.2012

Bruzzone, Lorenzo (Ed.) ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Image and signal processing for remote sensing XVII : 19 - 21 September 2011, Prague, Czech Republic; at SPIE Remote Sensing
Bellingham, WA: SPIE, 2011 (Proceedings of SPIE 8180)
ISBN: 978-0-8194-8807-7
Paper 818010
Conference "Image and Signal Processing for Remote Sensing" <17, 2011, Prague>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
SAR; TerraSAR-X; polarimetry; ship detection; segmentation; classification

Spaceborne SAR imagery offers high capability for wide-ranging maritime surveillance especially in situations, where AIS (Automatic Identification System) data is not available. Therefore, maritime objects have to be detected and optional information such as size, orientation, or object/ship class is desired. In recent research work,1 we proposed a SAR processing chain consisting of pre-processing, detection, segmentation, and classification for single-polarimetric (HH) TerraSAR-X StripMap images to finally assign detection hypotheses to class “clutter”, “non-ship”, “unstructured ship”, or “ship structure 1” (bulk carrier appearance) respectively “ship structure 2” (oil tanker appearance). In this work, we extend the existing processing chain and are now able to handle full-polarimetric (HH, HV, VH, VV) TerraSAR-X data. With the possibility of better noise suppression using the different polarizations, we slightly improve both the segmentation and the classification process. In several experiments we demonstrate the potential benefit for segmentation and classification. Precision of size and orientation estimation as well as correct classification rates are calculated individually for single- and quad-polarization and compared to each other.