TY - JOUR
T1 - A practical algorithm for the retrieval of floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar imagery
AU - Hwang, Byongjun
AU - Ren, Jinchang
AU - McCormack, Samuel
AU - Berry, Craig
AU - Ayed, Ismail Ben
AU - Graber, Hans C.
AU - Aptoula, Erchan
N1 - Funding Information:
We selected four cases for the validation of the algorithm in which “ground truth” data were manually produced. The selection was made to present diverse sea ice conditions in summer. All selected cases consisted of TerraSAR-X (TS-X) single-polarized (HH) StripMap (SM) images acquired during the summer of 2014, as part of the Marginal Ice Zone (MIZ) project, supported by the U.S. Office of Naval Research. For all selected TS-X SAR images, we found co-located high-resolution visible-band (HRV) images, a result of the declassification effort of the MEDEA group, from the U.S. Geological Survey Global Fiducials Library (GFL) (http://gfl.usgs.gov) (Table 1). The exact acquisition time for TS-X data is known, while such information is unknown for the GFL HRV images. Nonetheless the same ice floes between TS-X SAR and HRV images can be identified clearly as they did not move very much (Figure 2). For the selected SAR images, the intensity values were calibrated radiometrically and also scaled linearly to grayscale and re-projected to a UTM coordinate, prior to the application of the proposed algorithm. The pixel spacing of the original TS-X SM images is 1.25 m, and the pixel spacing of the HRV images is 1 m.
Funding Information:
We gratefully acknowledge the support from the Office of Naval Research and UK Natural Environment Research Council. We thank Center for Southeastern Tropical
Funding Information:
Funding was provided by the Office of Naval Research (grants N00014-12-1-0359, N00014-12-1-0448) as part of the Marginal Ice Zone, Department Research Initiative, ONR MIZ, by the UK Natural Environment Research Council (grants NE/M00600X/1, NE/L012707/1), and by the BAGEP Award of the Science Academy.
PY - 2017
Y1 - 2017
N2 - In this study, we present an algorithm for summer sea ice conditions that semi-automatically produces the floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar data. Currently, floe size distribution data from satellite images are very rare in the literature, mainly due to the lack of a reliable algorithm to produce such data. Here, we developed the algorithm by combining various image analysis methods, including Kernel Graph Cuts, distance transformation and watershed transformation, and a rule-based boundary revalidation. The developed algorithm has been validated against the ground truth that was extracted manually with the aid of 1-m resolution visible satellite data. Comprehensive validation analysis has shown both perspectives and limitations. The algorithm tends to fail to detect small floes (mostly less than 100 m in mean caliper diameter) compared to ground truth, which is mainly due to limitations in water-ice segmentation. Some variability in the power law exponent of floe size distribution is observed due to the effects of control parameters in the process of de-noising, Kernel Graph Cuts segmentation, thresholds for boundary revalidation and image resolution. Nonetheless, the algorithm, for floes larger than 100 m, has shown a reasonable agreement with ground truth under various selections of these control parameters. Considering that the coverage and spatial resolution of satellite Synthetic Aperture Radar data have increased significantly in recent years, the developed algorithm opens a new possibility to produce large volumes of floe size distribution data, which is essential for improving our understanding and prediction of the Arctic sea ice cover.
AB - In this study, we present an algorithm for summer sea ice conditions that semi-automatically produces the floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar data. Currently, floe size distribution data from satellite images are very rare in the literature, mainly due to the lack of a reliable algorithm to produce such data. Here, we developed the algorithm by combining various image analysis methods, including Kernel Graph Cuts, distance transformation and watershed transformation, and a rule-based boundary revalidation. The developed algorithm has been validated against the ground truth that was extracted manually with the aid of 1-m resolution visible satellite data. Comprehensive validation analysis has shown both perspectives and limitations. The algorithm tends to fail to detect small floes (mostly less than 100 m in mean caliper diameter) compared to ground truth, which is mainly due to limitations in water-ice segmentation. Some variability in the power law exponent of floe size distribution is observed due to the effects of control parameters in the process of de-noising, Kernel Graph Cuts segmentation, thresholds for boundary revalidation and image resolution. Nonetheless, the algorithm, for floes larger than 100 m, has shown a reasonable agreement with ground truth under various selections of these control parameters. Considering that the coverage and spatial resolution of satellite Synthetic Aperture Radar data have increased significantly in recent years, the developed algorithm opens a new possibility to produce large volumes of floe size distribution data, which is essential for improving our understanding and prediction of the Arctic sea ice cover.
KW - Arctic
KW - Image processing
KW - Sea ice floe size
KW - Synthetic Aperture Radar
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U2 - 10.1525/elementa.154
DO - 10.1525/elementa.154
M3 - Article
AN - SCOPUS:85055114300
VL - 5
JO - Elementa
JF - Elementa
SN - 2325-1026
M1 - 38
ER -