C-Band Radar Polarimetry—Useful for Detection of Icebergs in Sea Ice?

Dierking, Wolfgang; Wesche, Christine

doi:10.1109/tgrs.2012.2234756

Cited by 59 publications

(59 citation statements)

References 27 publications

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“…This is because a sea ice surface is typically rougher than a calm open water surface [73,74]. The co-polarization correlation coefficient is close to one for an isotropic scattering medium (e.g., multiyear ice or snow), while it decreases when the scattering medium is anisotropic (e.g., new ice and first-year ice) or the backscattering coefficients are close to the noise level [35]. Sea ice in the study area was composed of multiyear ice floes and snow-covered first-year ice [28], which show larger values of the co-polarization correlation coefficient than open water and melt ponds, which show very low backscattering signals.…”

Section: Polarimetric Signaturesmentioning

confidence: 99%

“…The use of a combination of polarimetric parameters computed from multi-polarization SAR can provide more physical properties of the ice surface than single polarimetric parameters [35,36]. Therefore, high-resolution polarimetric parameters can improve the performance of melt pond retrieval from SAR data, which can produce accurate statistics for melt ponds and sea ice.…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Han

Kim

et al. 2016

Remote Sensing

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Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approaches-decision trees (DT) and random forest (RF)-in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15ˆ15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.

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Section: Polarimetric Signaturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Han

Kim

et al. 2016

Remote Sensing

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“…Some special topics in sea ice research are ice drift ( [5,6]), ice concentration ( [7]), iceberg detection ( [8,9]), and sea state and wave propagation into sea ice ( [10,11]). Most research published so far on SAR based sea ice classification concentrates on single polarimetric data (e.g., [12][13][14][15][16][17][18][19][20][21]).…”

Section: Introductionmentioning

confidence: 99%

Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

Ressel

Singha

2016

Remote Sensing

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This work compares the polarimetric backscatter behavior of sea ice in spaceborne X-band and C-band Synthetic Aperture Radar (SAR) imagery. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features, which makes coherency matrix based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected ice types.

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“…We analyzed iceberg signatures in single C-band SAR images from the central Weddell Sea to develop a detection scheme [6]. We also tested the use of different polarizations and polarimetric parameters for iceberg detection [7]. Both, single and multi-polarization, methods have their pros and cons.…”

Section: Iceberg Monitoringmentioning

confidence: 99%

“…In the case of the icebergs presented here, the correlation coefficient and the phase difference between the HH-and VV-polarized SAR channel can be used for the detection of an iceberg surrounded by sea ice. However, when icebergs are surrounded by deformed sea ice or the iceberg has rolled over, their detection remains difficult [6,7].…”

Section: Iceberg Monitoringmentioning

confidence: 99%

From ice shelves to icebergs: Classification of calving fronts, iceberg monitoring and drift simulation

Wesche

Dierking

2014

2014 IEEE Geoscience and Remote Sensing Symposium

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Antarctica is surrounded by ice shelves and glaciers of different sizes. Satellite imagery shows different feature patterns (e.g. crevasses, rifts) at their surfaces, which control the shape and the size of icebergs that calve from their seaward edges. An edge detection method was used to map and classify the surface features, considering their orientation relative to the calving front. Calved icebergs can automatically be detected and then tracked on their way through the ocean using single and multi-polarized Synthetic Aperture Radar (SAR) images. Temporal gaps between subsequent SAR imagery can be closed by applying a simple wind-driven iceberg drift model.

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C-Band Radar Polarimetry—Useful for Detection of Icebergs in Sea Ice?

Cited by 59 publications

References 27 publications

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

From ice shelves to icebergs: Classification of calving fronts, iceberg monitoring and drift simulation

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