Operational sea ice monitoring with RADARSAT-2 - a glimpse into the future

Abreu, R. De; Flett, D.; Scheuchl, B.; Ramsay, B.

doi:10.1109/igarss.2003.1294092

Cited by 13 publications

(12 citation statements)

References 2 publications

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“…Furthermore, Partington et al noted that in an HV image, the depolarizing character of the thin ice distinguished it from the poorly depolarizing character of the OW [12]. This agreed with the study of De Abreu et al, which showed that there was a superior contrast between wind-roughened OW and ice in the HV polarization [11]. The greater depolarization of backscatter over ice allows easy separation from the water, which typically has a lower crosspolar response because its backscatter is dominated by surface scattering [11].…”

Section: Introductionsupporting

confidence: 58%

“…This agreed with the study of De Abreu et al, which showed that there was a superior contrast between wind-roughened OW and ice in the HV polarization [11]. The greater depolarization of backscatter over ice allows easy separation from the water, which typically has a lower crosspolar response because its backscatter is dominated by surface scattering [11]. Furthermore, the HV has also been shown to be better at separating the level from deformed ice [14] and firstyear ice from MYI [15].…”

Section: Introductionsupporting

confidence: 55%

“…Later, the polarimetric signatures of various sea ice types were investigated [11], [12]. These studies showed that HH provided a good contrast between new ice (NI) and gray ice due to its strong sensitivity to surface scattering differences [11].…”

Section: Introductionmentioning

confidence: 99%

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SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data

Liu

Guo

Zhang

2015

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

103

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An approach to sea ice classification using dual polarization RADARSAT-2 ScanSAR data is presented in this paper. It is based on support vector machine (SVM). In addition to backscatter coefficients and gray-level cooccurrence matrix (GLCM) texture features, sea ice concentration was introduced as a classification basis. To better analyze the backscatter information of sea ice types, we considered two steps that could improve the ScanSAR image quality, the noise floor stripe reduction and the incidence angle normalization. Then, effective GLCM texture characteristics from both polarizations were selected using the proper parameters. The third type of information, sea ice concentration, was extracted from the initial SVM classification result after the optimal SVM model was achieved from the training. The final result was generated by implementing the SVM twice and the decision tree once. Using this method, the classification was improved in two aspects, both of which were related to sea ice concentration. The results showed that the sea ice concentration parameter was effective in dealing with open water and in discriminating pancake ice from old ice. Finally, the maximum likelihood (ML) was run as a comparative test. In conclusion, the sea ice concentration parameter could play a role in SVM classification, and the whole process provided an effective way to classify sea ice using dual polarization ScanSAR data.

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Section: Introductionsupporting

confidence: 58%

Section: Introductionsupporting

confidence: 55%

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SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data

Liu

Guo

Zhang

2015

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

103

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“…The multipolarimetric (full-and dual-polarization) SAR imagery also offers improvements in OW, TI, and thick ice discrimination, compared to single-polarization imagery [6]- [8]. Dual-polarization data with HH and HV polarization can significantly enhance the interpretation accuracy and reduce the ambiguities in sea-ice classification [9], [10].…”

Section: Introductionmentioning

confidence: 97%

Antarctic Sea-Ice Classification Based on Conditional Random Fields From RADARSAT-2 Dual-Polarization Satellite Images

Zhu

Heygster

et al. 2016

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

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In January 2014, Chinese National Antarctic Research Expedition (CHINARE) 30th cruise raised public concern since the Xuelong, the Chinese polar research vessel, was trapped in the sea-ice zone (66°39 20.88 S, 144°25 2.28 E) in the vicinity of the Adélie Depression area on the east Antarctic continent. This event highlighted the importance of an operational sea-ice classification map for ice routing to serve ship navigation. In this paper, unprecedented Antarctic sea-ice classification algorithms from RADARSAT-2 satellite dual-polarization synthetic aperture radar (SAR) images were developed using the conditional random fields (CRF) approach by including multiple features from sea-ice concentration, gray-level cooccurrence matrix textures, polarization ratio, backscatter coefficients, and intensity data. Coincident RADARSAT-2 Satellite SAR datasets with five scenes were collected for ice classification into categories such as open water, thin ice, smooth first year ice, deformed first year ice, and old ice during the CHINARE-30th cruise. The effects of deformation, rafting, and ridging during the spring-summer transition period were overwhelmed by the spatial and contextual CRF models in combination with the rich features extracted for sea-ice classification. Four strategies including statistical distribution and region connection, multiple features and support vector machine (SVM) integrated into the CRF model are proposed to describe the sea-ice-type relationships among pixels. By conducting comparative experiments between the proposed methods and state-of-the-art sea-ice classification based on the SVM algorithm, the best was obtained from the SVM-based CRF (SVM-CRF) algorithm for sea-ice classification with respect to the three scenes from the Indian Ocean sector and two scenes of Pacific Ocean sectors including medium-resolution dual-polarization SAR imagery with a pixel spacing of 50 m and higher resolution dual-polarization SAR imagery with a pixel spacing of 6.25 m. Results indicate that the SVM-CRF approach has the capacity for improving sea-ice classification, which can provide accurate and reliable sea-ice class information for sea-ice analysis.

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“…The sensor noise floor for the cross-polarization channel can be a limiting factor in observing ice types with low backscatter, such as for discrimination of thin, new ice and open water areas. Therefore, the co-polarization option (HH and VV) of the Alternating Polarization mode may be preferable, as research has shown that the co-polarization ratio may also be useful for discriminating ice from open water areas (Scheuchl et al 2001;De Abreu et al 2003). An example of discrimination between ice and water from ENVISAT AP mode is shown in Figure 8-27.…”

Section: New Developments In Sea Ice Remote Sensingmentioning

confidence: 99%

Sea Ice Monitoring by Remote Sensing

Sandven¹,

Johannessen²,

Kloster³

2000

Encyclopedia of Analytical Chemistry

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Sea ice is defined as ice which is formed as a result of freezing of seawater. Sea ice occurs at the surface of the ocean in areas where the surface temperature is cooled to the freezing point, which is about −1.8°C for sea water with a salinity of about 35 parts per thousand. Ice formed in lakes and rivers and icebergs coming from glaciers and ice sheets are not defined as sea ice. Monitoring by remote sensing is defined as any measurement technique which can be used to observe sea ice repeatedly by instruments on board earth observation satellites. Monitoring also includes the process of making observational data available for users a short time after the observations have been obtained (i.e. a few hours). Remote sensing of ice can also be done with aircraft, submarines and other vehicles, but these techniques are not discussed in this article.

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Operational sea ice monitoring with RADARSAT-2 - a glimpse into the future

Cited by 13 publications

References 2 publications

SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data

SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data

Antarctic Sea-Ice Classification Based on Conditional Random Fields From RADARSAT-2 Dual-Polarization Satellite Images

Sea Ice Monitoring by Remote Sensing

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