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Open AccessArticle

Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases

1
Geoinformatics Department, Munich University of Applied Sciences, Karlstraße 6, D-80333 Munich, Germany
2
German Aerospace Center (DLR), Earth Observation Center (EOC), Oberpfaffenhofen, D-82234 Weßling, Germany
3
Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing (IPF), Englerstr. 7, D-76131 Karlsruhe, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 943; https://doi.org/10.3390/rs12060943
Received: 15 January 2020 / Revised: 9 March 2020 / Accepted: 10 March 2020 / Published: 14 March 2020
(This article belongs to the Special Issue Advances in Remote Sensing Image Fusion)
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. View Full-Text
Keywords: Kennaugh framework; quaternion; hypercomplex bases; image fusion; time series; change detection; SAR sharpening; data cube; analysis ready data; efficient archiving Kennaugh framework; quaternion; hypercomplex bases; image fusion; time series; change detection; SAR sharpening; data cube; analysis ready data; efficient archiving
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MDPI and ACS Style

Schmitt, A.; Wendleder, A.; Kleynmans, R.; Hell, M.; Roth, A.; Hinz, S. Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases. Remote Sens. 2020, 12, 943.

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