Evaluation of new spaceborne SAR sensors for sea-ice monitoring in the Baltic Sea

Eriksson, Leif E. B.; Borenäs, Karin; Dierking, Wolfgang; Berg, Anders; Santoro, Maurizio; Pemberton, Per; Lindh, Henrik; Karlson, Bengt

doi:10.5589/m10-020

Cited by 48 publications

(39 citation statements)

References 19 publications

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“…While most of the work carried out in the past was concerned with C-band data; confer, e.g., [7]- [21], we use ScanSAR data in X-band provided by the TerraSAR-X satellite (for technical specifications, see Table I). The shorter wavelength of the Xband signal yields a slightly different radar response compared with C-band (as delivered by RADARSAT or Sentinel) and reportedly much different response compared with L-band data [22]. This is mostly due to a much higher penetration depth of EM rays at L-band than at C-band or X-band.…”

Section: Introductionmentioning

confidence: 96%

A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images

Ressel

Frost

Lehner

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

108

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We examine the performance of an automated sea ice classification algorithm based on TerraSAR-X ScanSAR data. In the first step of our process chain, gray-level co-occurrence matrix(GLCM)-based texture features are extracted from the image. In the second step, these data are fed into an artificial neural network to classify each pixel. Performance of our implementation is examined by utilizing a time series of ScanSAR images in the Western Barents Sea, acquired in spring 2013. The network is trained on the initial image of the time series and then applied to subsequent images. We obtain a reasonable classification accuracy of at least 70% depending on the choice of our ice-type regime, when the incidence angle range of the training data matches that of the classified image. Computational cost of our approach is sufficiently moderate to consider this classification procedure a promising step toward operational, near-realtime ice charting.Index Terms-Earth and atmospheric sciences, pattern analysis, remote sensing, texture.

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Section: Introductionmentioning

confidence: 96%

A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images

Ressel

Frost

Lehner

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

108

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“…RI-1 has higher NESZ than RS-2, which could, in general, be a disadvantage for identifying sea ice types with low backscattering, e.g., grease ice. RI-1, however, has spatially finer resolution than RS-2, which might be an advantage in identifying more detailed sea ice structure like narrow ridges and rafting patterns [15]. Table 1.…”

Section: Sensor Properties and The Sar Scenesmentioning

confidence: 99%

Assessment of RISAT-1 and Radarsat-2 for Sea Ice Observations from a Hybrid-Polarity Perspective

2017

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Utilizing several Synthetic Aperture Radar (SAR) missions will provide a data set with higher temporal resolution. It is of great importance to understand the difference between various available sensors and polarization modes and to consider how to homogenize the data sets for a following combined analysis. In this study, a uniform and consistent analysis across different SAR missions is carried out. Three pairs of overlapping hybrid-and full-polarimetric C-band SAR scenes from the Radar Imaging Satellite-1 (RISAT-1) and Radarsat-2 satellites are used. The overlapping Radarsat-2 and RISAT-1 scenes are taken close in time, with a relatively similar incidence angle covering sea ice in the Fram Strait and Northeast Greenland in September 2015. The main objective of this study is to identify the similarities and dissimilarities between a simulated and a real hybrid-polarity (HP) SAR system. The similarities and dissimilarities between the two sensors are evaluated using 13 HP features. The results indicate a similar separability between the sea ice types identified within the real HP system in RISAT-1 and the simulated HP system from Radarsat-2. The HP features that are sensitive to surface scattering and depolarization due to volume scattering showed great potential for separating various sea ice types. A subset of features (the second parameter in the Stokes vector, the ratio between the HP intensity coefficients, and the α s angle) were affected by the non-circularity property of the transmitted wave in the simulated HP system across all the scene pairs. Overall, the best features, showing high separability between various sea ice types and which are invariant to the non-circularity property of the transmitted wave, are the intensity coefficients from the right-hand circular transmit and the linear horizontal receive channel and the right-hand circular on both the transmit and the receive channel, and the first parameter in the Stokes vector.

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“…X band is found to have good separation capabilities between first-year ice and old ice , between water and sea ice (Brath et al, 2013) and in detection of thin ice (Matsuoka et al, 2001). Results from the Baltic Sea suggest that the information content in C and X band are largely equivalent (Mäkynen and Hallikainen, 2004;Eriksson et al, 2010), while X band was found to add information when used in combination with C band in the Arctic Ocean (Brath et al, 2013).…”

Section: A S Fors Et Al: Late-summer Sea Ice Segmentation With Mulmentioning

confidence: 96%

Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band

Fors¹,

Brekke²,

Doulgeris³

et al. 2016

The Cryosphere

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Abstract. In this study, we investigate the potential of sea ice segmentation by C-and X-band multi-polarisation synthetic aperture radar (SAR) features during late summer. Five highresolution satellite SAR scenes were recorded in the Fram Strait covering iceberg-fast first-year and old sea ice during a week with air temperatures varying around 0 • C. Sea ice thickness, surface roughness and aerial photographs were collected during a helicopter flight at the site. Six polarimetric SAR features were extracted for each of the scenes. The ability of the individual SAR features to discriminate between sea ice types and their temporal consistency were examined. All SAR features were found to add value to sea ice type discrimination. Relative kurtosis, geometric brightness, cross-polarisation ratio and co-polarisation correlation angle were found to be temporally consistent in the investigated period, while co-polarisation ratio and co-polarisation correlation magnitude were found to be temporally inconsistent. An automatic feature-based segmentation algorithm was tested both for a full SAR feature set and for a reduced SAR feature set limited to temporally consistent features. In C band, the algorithm produced a good late-summer sea ice segmentation, separating the scenes into segments that could be associated with different sea ice types in the next step. The X-band performance was slightly poorer. Excluding temporally inconsistent SAR features improved the segmentation in one of the X-band scenes.

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Evaluation of new spaceborne SAR sensors for sea-ice monitoring in the Baltic Sea

Cited by 48 publications

References 19 publications

A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images

A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images

Assessment of RISAT-1 and Radarsat-2 for Sea Ice Observations from a Hybrid-Polarity Perspective

Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band

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