The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation

Schmitt, Andreas; Wendleder, Anna; Hinz, Stefan

doi:10.1016/j.isprsjprs.2015.01.007

Cited by 79 publications

(122 citation statements)

References 51 publications

(55 reference statements)

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“…Therefore, not only the polarizations VV and VH were applied, but also their combination. By combining polarizations, the effects, such as different sensitivities of the polarisations to objects, can also be intensified, e.g., by mathematically induced stretching of the data [75][76][77]. The intensity of the amplification was quantified using the RF method (Figure 8).…”

Section: Multi-temporal Characteristics and Time Series Featuresmentioning

confidence: 99%

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

et al. 2018

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Abstract:The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth's surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.

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Section: Multi-temporal Characteristics and Time Series Featuresmentioning

confidence: 99%

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

et al. 2018

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“…Schmitt et al (2015) developed a polarimetric decomposition of the Kennaugh matrix and applied its elements to dual-and full-pol data of any wavelength, and confirmed that most of the information content could be deduced from HH and VV polarized modes with stable phase relation. For the dual-pol mode the decomposition generates four Kennaugh elements as shown in equations (1) - (4) (Schmitt et al, 2015):…”

Section: Kennaugh Element Frameworkmentioning

confidence: 82%

“…The normalized Kennaugh elements framework recently introduced by Schmitt et al (2015) is a method that can be applied to dual-pol data of any wavelength, hence it has been chosen in this study for exploiting the capabilities of dual-pol data for the monitoring of intertidal flats, especially for the detection and classification of sediments and bivalve beds.…”

Section: Introductionmentioning

confidence: 99%

A New Sar Classification Scheme for Sediments on Intertidal Flats Based on Multi-Frequency Polarimetric Sar Imagery

Wang

Gade

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:We present a new classification scheme for muddy and sandy sediments on exposed intertidal flats, which is based on synthetic aperture radar (SAR) data, and use ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band) fully polarimetric SAR imagery to demonstrate its effectiveness. Four test sites on the German North Sea coast were chosen, which represent typical surface compositions of different sediments, vegetation, and habitats, and of which a large amount of SAR is used for our analyses. Both Freeman-Durden and Cloude-Pottier polarimetric decomposition are utilized, and an additional descriptor called Double-Bounce Eigenvalue Relative Difference (DERD) is introduced into the feature sets instead of the original polarimetric intensity channels. The classification is conducted following Random Forest theory, and the results are verified using ground truth data from field campaigns and an existing classification based on optical imagery. In addition, the use of Kennaugh elements for classification purposes is demonstrated using both fully and dual-polarization multi-frequency and multi-temporal SAR data. Our results show that the proposed classification scheme can be applied for the discrimination of muddy and sandy sediments using L-, C-, and X-band SAR images, while SAR imagery acquired at short wavelengths (C-and X-band) can also be used to detect more detailed features such as bivalve beds on intertidal flats.

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“…These effects can be corrected during the ortho-rectification using a digital elevation model. For the left image only one look, whereas for the right image 64 looks are applied (Schmitt et al, 2015).…”

Section: Ortho-rectificationmentioning

confidence: 99%

“…Subset of a TerraSAR-X spotlight acquisition over the harbor area of Mannheim-Ludwigshafen in Germany. For the left image only one number of looks is adapted, whereas for the right image multi-scale multi-looking is adapted (Schmitt et al, 2015).…”

Section: Radiometric Calibrationmentioning

confidence: 99%

Long-Term Monitoring of Water Dynamics in the Sahel Region Using the Multi-Sar-System

Bertram

Wendleder

Schmitt

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Fresh water is a scarce resource in the West-African Sahel region, seasonally influenced by droughts and floods. Particularly in terms of climate change, the importance of wetlands increases for flora, fauna, human population, agriculture, livestock and fishery. Hence, access to open water is a key factor. Long-term monitoring of water dynamics is of great importance, especially with regard to the spatio-temporal extend of wetlands and drylands. It can predict future trends and facilitate the development of adequate management strategies. Lake Tabalak, a Ramsar wetland of international importance, is one of the most significant ponds in Niger and a refuge for waterbirds. Nevertheless, human population growth increased the pressure on this ecosystem, which is now degrading for all uses. The main objective of the study is a long-term monitoring of the Lake Tabalak's water dynamics to delineate permanent and seasonal water bodies, using weather-and daytime-independent multi-sensor and multi-temporal Synthetic Aperture Radar (SAR) data available for the study area. Data of the following sensors from 1993 until 2016 are used: Sentinel-1A, TerraSAR-X, ALOS PALSAR-1/2, Envisat ASAR, RADARSAT-1/2, and ERS-1/2. All SAR data are processed with the Multi-SAR-System, unifying the different characteristics of all above mentioned sensors in terms of geometric, radiometric and polarimetric resolution to a consistent format. The polarimetric representation in Kennaugh elements allows fusing single-polarized data acquired by older sensors with multi-polarized data acquired by current sensors. The TANH-normalization guarantees a consistent and therefore comparable description in a closed data range in terms of radiometry. The geometric aspect is solved by projecting all images to an earth-fixed coordinate system correcting the brightness by the help of the incidence angle. The elevation model used in the geocoding step is the novel global model produced by the TanDEM-X satellite mission. The advantage of the Multi-SAR-System is that it comprises ortho-rectification, radiometric enhancement, normalization and Kennaugh decomposition, independent from sensors, modes, polarizations or acquisition date of SAR data. In addition, optical satellite data can be included as well, to fill gaps where SAR data are missing due to the special normalization scheme. This kind of pre-processing is exclusively implemented at the Earth Observation Center of the German Aerospace Center in Oberpfaffenhofen, Germany. Therefore, the dynamic change of the open water of the Lake Tabalak could be classified over dry and rainy seasons and years, using different SAR data. The study provides a unique database and contributes to a better understanding of wetland systems in the Sahel region influenced by human pressure and climate change.

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The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation

Cited by 79 publications

References 51 publications

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

A New Sar Classification Scheme for Sediments on Intertidal Flats Based on Multi-Frequency Polarimetric Sar Imagery

Long-Term Monitoring of Water Dynamics in the Sahel Region Using the Multi-Sar-System

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