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

Abstract

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.

An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series

“…Additionally, preprocessing techniques are implemented to prepare the data in a format suitable for AI algorithms. This preprocessing may include techniques like explicit normalization, which ensures that data are scaled or adjusted to be in a standardized format [41]. Normalization is particularly important for some machine learning techniques, such as support vector machines, which rely on the data being in a specific range or format to work effectively.…”

“…In order to fuse multi-polarized SAR and multi-spectral optical data, a common radiometric frame is necessary. One most interesting approach was mentioned in the context of SARsharpening [53] and later on explained as hyper-complex bases (HCBs) in detail [41]. The basic idea is to generate Kennaugh-like elements from the multi-spectral reflectances of Sentinel-2 that are compatible with the Kennaugh elements of Sentinel-1.…”

“…This step reduces the typical dominance of the NIR channel apparent in most optical images. Then, the reflectances are transformed into the Kennaugh space by a simple linear combination [41]. The Kennaugh-like elements of Sentinel-2 are thus composed of one total reflectance and three spectral elements in complete analogy to the total intensity and the three polarimetric elements of Sentinel-1.…”

Seasonal Evolution of Supraglacial Lakes on Baltoro Glacier From 2016 to 2020

“…The PlanetScope Analytic Ortho Scene Products (Level 3B) were already downloaded as orthorectified and atmospherically corrected Surface Reflectance (SR) data. Sentinel-1 Interferometric Wide Swath C-band and TerraSAR-X ScanSAR X-band data were processed to normalized radar backscatter Analysis Ready Data (ARD) using the Multi-SAR System (Schmitt et al, 2015;Schmitt et al, 2020). For the orthorectification, the 3-arcsecond Copernicus-DEM (GLO-90) (Airbus, 2020) was used as it had the best geometric correspondence to Sentinel-2 and PlanetScope data.…”

Long-term Land Cover Change Detection Using Multisensor and Multiresolution Remote Sensing Images: A Case Study of Chang’an University, China

“…To solve the problems mentioned above, several attempts have been made to combine multisensor and multiresolution remote sensing images to obtain the best change detection results for specific LULC change detection applications. For example, Schmitt et al (14) developed a new consistent framework for the production, archiving, and provision of analysis-ready data from multisensor and multitemporal images acquired from satellites and subsequent image fusion. Deng et al (15) combined principal component analysis of multisensor satellite images from SPOT and Landsat-7 data, and used a supervised classification method to detect and analyze the LULC changes in the city center of Hangzhou.…”