Characterization of sea ice types using synthetic aperture radar

Lyden, J. D.; Burns, Barbara A.; Maffett, Andrew Lewis

doi:10.1109/tgrs.1984.6498983

Cited by 29 publications

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“…Paper number 91JC02652. 0148-0227/92/91JC-02652505.00 image analysis approaches have been applied to the ice signatures to examine the types of information available in the SAR imagery for the automatic extraction of ice types [e.g., Lyden et al, 1984;Wackerman et al, 1988].…”

Section: Introductionmentioning

confidence: 99%

“…An advantage of using the lower-resolution image products is the low level of multiplicative noise or speckle that is present after the averaging process. Thus it is possible to avoid special segmentation techniques [Lyden, 1984;Wackerman et al, 1988] which account for speckle statistics to minimize classification errors. This simplifies the task of the automated procedure to finding optimum thresholds for segmentation of the different ice types using the expected microwave responses at C band.…”

Section: Introductionmentioning

confidence: 99%

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Identification of sea ice types in spaceborne synthetic aperture radar data

Rignot²,

et al. 1992

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An approach for identification of sea ice types in spaceborne synthetic aperture radar (SAR) image data is presented. The unsupervised classification approach involves cluster analysis for segmentation of the image data followed by cluster labeling based on previously defined look-up tables containing the expected backscatter signatures of different ice types measured by land-based scatterometer. The particular look-up table used for labeling a segmented image is selected based on the seasonal and meteorological conditions at the time of data acquisition. The extensive scatterometer observations and experience accumulated in field campaigns during the last 10 years were used to construct these look-up tables. These tables are expected to evolve as sea ice observations from the European ERS-1 SAR become available. This paper presents the classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics. Results using both aircraft and simulated ERS-1 SAR data are presented. The results are compared to limited field ice property measurements and coincident passive microwave imagery. An algorithm based on this experimental approach has been implemented in the geophysical processor system at the Alaska SAR Facility for classification of sea ice data in ERS-1 C band SAR data. The importance of an integrated postlaunch program for validation and improvement of this approach is discussed. INTRODUCTIONThe derivation of valuable information on sea ice properties from radar imagery has increased steadily over the last decade [Carsey, 1989] These SAR studies of sea ice have examined imagery obtained from aircraft and spaceborne SAR systems, usually in combination with other sensors, in situ measurements of ice and snow conditions, and near-surface scatterometer and radiometer measurements. The radars have operated over a wide range of frequencies, incidence angles, and polarizations. During this decade, single-channel spaceborne S ARs are to be launched on the European ERS-1 in 1991, the Japanese ERS-1 in 1992, and the Canadian RADARSAT in 1994. These satellites will provide the first opportunities for the extensive spaceborne monitoring of the polar regions with a SAR since Seasat (which operated for 3• months in 1978). At the end of this decade, NASA has proposed to launch the EOS (Earth Observing System) SAR (a multifrequency, multipolarization SAR) as an integral component of the Mission to Planet Earth, a comprehensive suite of satellite instruments designed to examine global climate change, of which sea ice is a key and supposedly dramatic indicator.In response to these opportunities for examining the polar regions with high-resolution, all-weather spaceborne SARs, the Alaska SAR Facility (ASF) has been implemented at the University of Alaska, Fairbanks, to receive, process, and archive SAR data from these satellites and to generate sea ice geophysical products from the data [ This paper focuses on the ice classification algo...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of sea ice types in spaceborne synthetic aperture radar data

Rignot²,

et al. 1992

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“…The second purpose was to expand the scope of applicability of the derived conclusions. This aspect was particularly important since previous studies [e.g., Lyden et al, 1984] were limited to a single SAR scene, and that restricted the applicability of the concluded information.Section 2 in this paper describes SAR images which were used in the study. Section 3 introduces the definitions of the texture parameters with some elaboration on their meanings, and it also addresses the outline of the analysis method.…”

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confidence: 99%

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Evaluation of second‐order texture parameters for sea ice classification from radar images

Shokr¹

1991

J. Geophys. Res.

116

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With the advent of airborne and spaceborne synthetic aperture radar (SAR) systems, sea ice classification from SAR images has become an important research subject. Since gray tone alone has proven to be of limited capability in differentiating ice types, texture has naturally become an attractive avenue to explore. Accordingly, performance of texture quantification parameters as related to their ability to discriminate ice types has to be examined. SAR image appearance depends on radar parameters involved in the image construction procedures from the doppler history record. Therefore the feasibility of using universal texture/ice type relationships that hold for all combinations of radar parameters also has to be investigated. To that end, imagery data from three different SAR systems were used in this study. Five conventional texture parameters, derived from the gray level co‐occurrence matrix (GLCM), were examined. Two of them were modified to ensure their invariant character under linear gray tone transformations. Results indicated that all parameters were highly correlated. The parameters did not, in general, vary with the computational variables used in generating co‐occurrence matrices. Ice types can be identified uniquely by the mean value of any texture parameter. The relatively high variability of texture parameters, however, confuses ice discrimination, particularly of smoother ice types. Ice classification was conducted using a per‐pixel maximum likelihood supervised scheme. When texture was combined with gray tone, the overall average classification accuracy was improved. Texture was successful in improving the classification accuracy of multiyear ice but was less promising in discriminating first‐season ice types. The best two GLCM texture parameters, according to the computed overall average classification accuracies, were the inverse difference moment and the entropy. A brief description of GLCM texture parameters as related to ice's physical characteristics is presented.

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“…Given the processed imagery, our ice classification scheme consisted of a training area technique based on mean and standard deviation statistics similar to that used by Lyden et al [1984]. Specifically, within each data take, we calculated…”

Section: The Jpl Techniquementioning

confidence: 99%

Shuttle Imaging Radar B (SIR‐B) Weddell Sea ice observations: A comparison of SIR‐B and scanning multichannel microwave radiometer ice concentrations

et al. 1987

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The October 1984 Shuttle lmaging Radar B (SIR-B) flight made three radar passes over the Weddell Sea ice, providing the first high-resolution look at the Weddell marginal ice zone properties. Using these data, this paper discusses the effect of ocean waves on the radar return at the ice edge and compares ice concentrations derived from the SIR-B with coincident concentrations from the Nimbus 7 scanning multichannel microwave radiometer (SMMR). The comparison of the SIR and SMMR concentrations is possible because SIR cross-track width and the diameter of the SMMR 37-GHz integrated field-of-view are both about 30 km. The SIR ice concentrations are computed in two ways: first, using a training area classification scheme at the Jet Propulsion Laboratory (JPL); second, using a manual classification method at the Scott Polar Research Institute. The SMMR ice concentrations are calculated using the Goddard Space Flight Center algorithm. At the ice edge, where there were no coincident SMMR data and where ice bands predominated to yield an ice concentration of the order of 10% or less, comparison of the two different analysis techniques on the same images showed that, for the JPL technique to avoid classifying some of the open water as ice, two classes of open water must be defined. These two classes accounted for the rougher ocean surface upwind of the bands and the smoother downwind surface. In the ice interior, comparison of the coincident SIR and SMMR ice concentrations shows that for concentrations greater than 40%, which was the smallest concentration jointly observed, the mean difference between the two data sets for 12 points is 2% and the standard deviation is 7%.

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Characterization of sea ice types using synthetic aperture radar

Cited by 29 publications

References 0 publications

Identification of sea ice types in spaceborne synthetic aperture radar data

Identification of sea ice types in spaceborne synthetic aperture radar data

Evaluation of second‐order texture parameters for sea ice classification from radar images

Shuttle Imaging Radar B (SIR‐B) Weddell Sea ice observations: A comparison of SIR‐B and scanning multichannel microwave radiometer ice concentrations

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