International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W2, 223–228, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W2-223-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W2, 223–228, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W2-223-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  16 Nov 2017

16 Nov 2017

A NEW SAR CLASSIFICATION SCHEME FOR SEDIMENTS ON INTERTIDAL FLATS BASED ON MULTI-FREQUENCY POLARIMETRIC SAR IMAGERY

W. Wang1,2,3 and M. Gade2 W. Wang and M. Gade
  • 1Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 100101 Beijing, China
  • 2Institut für Meereskunde, Universität Hamburg, 20146 Hamburg, Germany
  • 3University of Chinese Academy of Sciences, 100049 Beijing, China

Keywords: DERD, Intertidal flats, Wadden Sea, Oyster beds, Polarimetric SAR, Multi-frequency

Abstract. We present a new classification scheme for muddy and sandy sediments on exposed intertidal flats, which is based on synthetic aperture radar (SAR) data, and use ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band) fully polarimetric SAR imagery to demonstrate its effectiveness. Four test sites on the German North Sea coast were chosen, which represent typical surface compositions of different sediments, vegetation, and habitats, and of which a large amount of SAR is used for our analyses. Both Freeman-Durden and Cloude-Pottier polarimetric decomposition are utilized, and an additional descriptor called Double-Bounce Eigenvalue Relative Difference (DERD) is introduced into the feature sets instead of the original polarimetric intensity channels. The classification is conducted following Random Forest theory, and the results are verified using ground truth data from field campaigns and an existing classification based on optical imagery. In addition, the use of Kennaugh elements for classification purposes is demonstrated using both fully and dual-polarization multi-frequency and multi-temporal SAR data. Our results show that the proposed classification scheme can be applied for the discrimination of muddy and sandy sediments using L-, C-, and X-band SAR images, while SAR imagery acquired at short wavelengths (C- and X-band) can also be used to detect more detailed features such as bivalve beds on intertidal flats.