New article: Object-Based Detection of Linear Kinematic Features in Sea Ice (Open Access)

Stefanie Linow and Wolfgang Dierking’s new article “Object-Based Detection of Linear Kinematic Features in Sea Ice” was recently published in the Journal Remote Sensing.


Inhomogenities in the sea ice motion field cause deformation zones, such as leads, cracks and pressure ridges. Due to their long and often narrow shape, those structures are referred to as Linear Kinematic Features (LKFs). In this paper we specifically address the identification and characterization of variations and discontinuities in the spatial distribution of the total deformation, which appear as LKFs. The distribution of LKFs in the ice cover of the polar oceans is an important factor influencing the exchange of heat and matter at the ocean-atmosphere interface. Current analyses of the sea ice deformation field often ignore the spatial/geographical context of individual structures, e.g., their orientation relative to adjacent deformation zones. In this study, we adapt image processing techniques to develop a method for LKF detection which is able to resolve individual features. The data are vectorized to obtain results on an object-based level. We then apply a semantic postprocessing step to determine the angle of junctions and between crossing structures. The proposed object detection method is carefully validated. We found a localization uncertainty of 0.75 pixel and a length error of 12% in the identified LKFs. The detected features can be individually traced to their geographical position. Thus, a wide variety of new metrics for ice deformation can be easily derived, including spatial parameters as well as the temporal stability of individual features.


Linow, Stefanie; Dierking, Wolfgang.2017. "Object-Based Detection of Linear Kinematic Features in Sea Ice." Remote Sens. 9, no. 5: 493.

Figure from Linow, Stefanie; Dierking, Wolfgang. 2017. 



Linow, S., Dierking, W. (2017): Object-Based Detection of Linear Kinematic Features in Sea Ice. Remote Sensing 2017, 9(5), 493. DOI: 10.3390/rs9050493 [open access]

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