Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases
Autor: | Stefan Hinz, Anna Wendleder, Rüdiger Kleynmans, Andreas Schmitt, Maximilian Hell, Achim Roth |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
efficient archiving
quaternion 010504 meteorology & atmospheric sciences Computer science Science 0211 other engineering and technologies data cube 02 engineering and technology 01 natural sciences Kennaugh framework image fusion Data cube Robustness (computer science) SAR sharpening ddc:550 Dynamik der Landoberfläche change detection hypercomplex bases 021101 geological & geomatics engineering 0105 earth and related environmental sciences Image fusion Hypercomplex number Basis (linear algebra) business.industry Pattern recognition Data set Earth sciences General Earth and Planetary Sciences Artificial intelligence time series analysis ready data business Multi-source Data compression |
Zdroj: | Remote Sensing; Volume 12; Issue 6; Pages: 943 Remote sensing, 12 (6), 943 Remote Sensing, Vol 12, Iss 6, p 943 (2020) |
ISSN: | 2072-4292 |
DOI: | 10.3390/rs12060943 |
Popis: | 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. |
Databáze: | OpenAIRE |
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