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As the number of space-borne SAR sensors increases, a rising number of different SAR acquisition modes is in use, resulting in a higher variation within the image products. This variability in acquisition geometry, radiometry, and last but not least polarimetry raises the need for a consistent SAR image description incorporating all available sensors and acquisition modes. This paper therefore introduces the framework of the Kennaugh elements to comparably represent all kinds of multi-scale, multi-temporal, multi-polarized, multi-frequency, and hence, multi-sensor data in a consistent mathematical framework. Furthermore, a novel noise model is introduced that estimates the significance and thus the (polarimetric) information content of the Kennaugh elements. This facilitates an advanced filtering approach, called multi-scale multi-looking, which is shown to improve the radiometric accuracy while preserving the geometric resolution of SAR images. The proposed methodology is finally demonstrated using sample applications that include TerraSAR-X (X-band), Envisat-ASAR, RADARSAT-2 (C-band) and ALOS-PALSAR (L-band) data as well as the combination of all three frequencies. Thus the suitability of the Kennaugh element framework for practical use in proved for advanced SAR remote sensing. [ABSTRACT FROM AUTHOR]

This article spanned a new, consistent framework for production, archiving, and provision of analysis ready data (ARD) from multi-source and multi-temporal satellite acquisitions and an subsequent image fusion. The core of the image fusion was an orthogonal transform of the reflectance channels from optical sensors on hypercomplex bases delivered in Kennaugh-like elements, which are well-known from polarimetric radar. In this way, SAR and Optics could be fused to one image data set sharing the characteristics of both: the sharpness of Optics and the texture of SAR. The special properties of Kennaugh elements regarding their scaling—linear, logarithmic, normalized—applied likewise to the new elements and guaranteed their robustness towards noise, radiometric sub-sampling, and therewith data compression. This study combined Sentinel-1 and Sentinel-2 on an Octonion basis as well as Sentinel-2 and ALOS-PALSAR-2 on a Sedenion basis. The validation using signatures of typical land cover classes showed that the efficient archiving in 4 bit images still guaranteed an accuracy over 90% in the class assignment. Due to the stability of the resulting class signatures, the fuzziness to be caught by Machine Learning Algorithms was minimized at the same time. Thus, this methodology was predestined to act as new standard for ARD remote sensing data with an subsequent image fusion processed in so-called data cubes. [ABSTRACT FROM AUTHOR]