Volume IV-1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 133-140, 2018
https://doi.org/10.5194/isprs-annals-IV-1-133-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 133-140, 2018
https://doi.org/10.5194/isprs-annals-IV-1-133-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  26 Sep 2018

26 Sep 2018

SAR-SHARPENING IN THE KENNAUGH FRAMEWORK APPLIED TO THE FUSION OF MULTI-MODAL SAR AND OPTICAL IMAGES

A. Schmitt1 and A. Wendleder2 A. Schmitt and A. Wendleder
  • 1Munich University of Applied Sciences, Department of Geoinformatics, Karlstraße 6, D-80333 Munich, Germany
  • 2German Aerospace Center (DLR), Earth Observation Center, Oberpfaffenhofen, D-82234 Weßling, Germany

Keywords: SAR, Optical, Image fusion, Image sharpening, Polarimetry, Multispectral imaging, Multi-sensor data fusion

Abstract. The Kennaugh framework turned out to be a powerful tool for the preparation of multi-sensor SAR data during the last years. Using intensity-based (an-) isotropic diffusion algorithms like the Multi-scale Multi-looking or the Schmittlets, even robust pre-classification change detection from multi-polarized images is enabled. The only missing point so far, namely the integration of multi-mode SAR data in one image, is accomplished in this article. Furthermore, the Kennaugh decomposition is extended to multi-spectral data as well. Hence, arbitrary Kennaugh elements, be it from SAR or optical images, can be fused. The mathematical description of the most general image fusion is derived and applied to four scenarios. The validation section considers the distribution of mean and gradient in the original and the fused images by the help of scatter plots. The results prove that the fused images adopt the spatial gradient of the input image with a higher geometric resolution and preserve the local mean of the input image with a higher polarimetric and thus also radiometric resolution. Regarding the distribution of the entropy and alpha angle, the fused images are always characterized by a higher variance in the entropy-alpha-plane and therewith, a higher resolution in the polarimetric domain. The proposed algorithm guarantees optimal information integration while ensuring the separation of intensity and polarimetric/spectral information. The Kennaugh framework is ready now to be used for the sharpening of multi-sensor image data in the spatial, radiometric, polarimetric, and even spectral domain.