Automated Wetland Delineation From Multi-Frequency and Multi-Polarized Sar Images in High Temporal and Spatial Resolution

Moser, L.; Schmitt, Andreas; Wendleder, Anna

doi:10.5194/isprsannals-iii-8-57-2016

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…The change detection capability was used for mapping wind throw areas in mid-European forests from TerraSAR-X images [14]. The usefulness of the normalized Kennaugh elements in the context of wetland remote sensing especially in semi-arid regions was proven several times using TerraSAR-X, RADARSAT-2, and even archived data of RADARSAT-1 and ENVISAT-ASAR [15][16][17]. Due to its flexibility concerning the input data, different polarization combinations could be evaluated at the same time for surface water monitoring purposes [18].…”

Section: Applications Based On Multi-sar Systemmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Applications Based On Multi-sar Systemmentioning

confidence: 99%

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

et al. 2020

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“…Smooth open water is characterized by low SAR backscatter values due to its specular surface. In comparison, TFV shows a significant increase in backscatter, especially in the VV (vertical/vertical) polarization, which is caused by the complex double-or multi-bounce interaction between smooth open water surfaces and the structure of vegetation (e.g., tree trunks, stems) [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features

Tsyganskaya

Martinis

Marzahn

2019

Water

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Synthetic Aperture Radar (SAR) is particularly suitable for large-scale mapping of inundations, as this tool allows data acquisition regardless of illumination and weather conditions. Precise information about the flood extent is an essential foundation for local relief workers, decision-makers from crisis management authorities or insurance companies. In order to capture the full extent of the flood, open water and especially temporary flooded vegetation (TFV) areas have to be considered. The Sentinel-1 (S-1) satellite constellation enables the continuous monitoring of the earths surface with a short revisit time. In particular, the ability of S-1 data to penetrate the vegetation provides information about water areas underneath the vegetation. Different TFV types, such as high grassland/reed and forested areas, from independent study areas were analyzed to show both the potential and limitations of a developed SAR time series classification approach using S-1 data. In particular, the time series feature that would be most suitable for the extraction of the TFV for all study areas was investigated in order to demonstrate the potential of the time series approaches for transferability and thus for operational use. It is shown that the result is strongly influenced by the TFV type and by other environmental conditions. A quantitative evaluation of the generated inundation maps for the individual study areas is carried out by optical imagery. It shows that analyzed study areas have obtained Producer’s/User’s accuracy values for TFV between 28% and 90%/77% and 97% for pixel-based classification and between 6% and 91%/74% and 92% for object-based classification depending on the time series feature used. The analysis of the transferability for the time series approach showed that the time series feature based on VV (vertical/vertical) polarization is particularly suitable for deriving TFV types for different study areas and based on pixel elements is recommended for operational use.

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