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26 October 2011 Comparison of using single- or multi-polarimetric TerraSAR-X images for segmentation and classification of man-made maritime objects
Michael Teutsch, Günter Saur
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Proceedings Volume 8180, Image and Signal Processing for Remote Sensing XVII; 818010 (2011) https://doi.org/10.1117/12.898222
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic
Abstract
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, 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.
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Michael Teutsch and Günter Saur "Comparison of using single- or multi-polarimetric TerraSAR-X images for segmentation and classification of man-made maritime objects", Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 818010 (26 October 2011); https://doi.org/10.1117/12.898222
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KEYWORDS
Image segmentation
Synthetic aperture radar
Image classification
Error analysis
Polarimetry
Image processing
Principal component analysis
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