GeoWAM-LB: Neue Geodaten zur Verbesserung des Wassermanagements tidebeeinflusster Küstenbereiche - Langeoog und Baltrum - F-SAR Datenerfassung und Produktbeschreibung

NLWKN und DLR haben im Zeitraum 2019-2022 im Rahmen des mFund-Verbundprojektes GeoWAM die Kartierung trockenfallender Watt- und Vorlandbereiche mittels flugzeuggestützter SAR Interferometrie untersucht und dabei multi-temporale Daten der Testgebiete Medemrinne an der Elbe und Otzumer Balje mit Spiekeroog analysiert. Zur weiteren Durchführung von F&E Aufgaben im Rahmen küstenschutzrelevanter Aufgabenstellungen, insbesondere der Übertragbarkeitsanalyse der im GeoWAM Projekt entwickelten Verfahren, hat DLR-HR im April 2022 weitere Gebiete an den Inseln Langeoog und Baltrum beflogen. Die bedarfs- und nutzergerechte Datenprozessierung der am 20. und 21. April 2022 durchgeführten F-SAR Befliegung und die Beschreibung der errechneten Datenprodukte ist Gegenstand dieses technischen Berichts

Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR\u27s airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/170

Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR's airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/1700

The Short-Time Beltrami Kernel for Despeckling Polarimetric SAR Data

In this paper, we adapt the Short-time Beltrami (STB) kernel used to smooth manifolds or images painted on manifolds to use the covariance matrices found in polarimetric synthetic aperture (PolSAR) data to reduce speckle noise. The proposed method functions similar to a region growing algorithm finding similar covariance matrices and weighting them accordingly. Preliminary results from the experiments show that the proposed method delivers a good compromise between the preservation of the spatial resolution and smoothing of homogeneous zones

Potentialanalyse von SAR-basierten Oberflächenmodellen in Monitoring- und Analyseprozessen in tidebeeinflussten Gebieten

Zahlreiche notwendige Monitoringaufgaben im Nationalpark deutsches Wattenmeer basieren auf regelmäßig erstellten, hoch aufgelösten Geodaten in Form von digitalen Gelände- und Oberflachenmodellen. Das Wassermanagement im Wattenmeerbereich der Nordsee erfordert das stetige Monitoring der unter anderem tidebedingten Vegetations- und Topographie Veränderungen, welche derzeit zumeist mit dem etablierten Verfahren des Airborne Laserscanning erfasst werden. Ergänzend zur etablierten Laserscanmethode wird im Projekt GeoWAM die Erfassungsmethode der flugzeuggestutzten Radarinterferometrie anhand von drei Befliegungskampagnen in zwei Testgebieten optimiert und das Potential der abgeleiteten, radarbasierten Oberflachenmodelle für die notwendigen Monitoringaufgaben im Küstengebiet aus Anwendersicht untersucht. Die im Projektverlauf erzielte Qualitatsverbesserung wird im Folgenden präsentiert und eine kritische Betrachtung der Projektergebnisse führt zum Fazit, dass eine ähnlich gute Datenqualität, wie sie in der letzten Befliegungskampagne erreicht werden konnte, als Voraussetzung für eine erfolgreiche Veranderungsanalyse angesehen werden muss. Werden Oberflachenmodelle mit einer geringeren Datenqualität in die Analysen einbezogen, fuhren Datenlücken und Artefakte zu erschwerten Analysebedingungen. Die Ergebnisse der letzten Befliegungskampagne zeigen jedoch ein vielversprechendes Potential für zukünftige Monitoringsaufgaben im tidebeeinflussten Kustengebiet

ICESAR 2019 - A Study on Sea Ice based on F-SAR XCL-Band Data

This paper summarizes study results obtained about the characteristics of sea ice in the Davis Strait off the coast of Baffin Island in 2019. The study, also referred to as ICESAR 2019, is based on multi-frequency and interferometric data collected by the DLR F-SAR airborne radar in the course of the PermASAR campaign in the Canadian Arctic with the aim of improving knowledge on the radar properties of sea ice at different wavelengths and polarisationsThis paper summarizes study results obtained about the characteristics of sea ice in the Davis Strait off the coast of Baffin Island in 2019. The study, also referred to as ICESAR 2019, is based on multi-frequency and interferometric data collected by the DLR F-SAR airborne radar in the course of the PermASAR campaign in the Canadian Arctic with the aim of improving knowledge on the radar properties of sea ice at different wavelengths and polarisation