Abstract
A technique is presented that automates the direction characterization of curvilinear features in multidimensional solar imaging datasets. It is an extension of the Rolling Hough Transform (RHT) technique presented by Clark, Peek, and Putman (Astrophys. J. 789, 82, 2014), and it excels at rapid quantification of spatial and spatiotemporal feature orientation even for applications with a low signal-to-noise ratio. It operates on a pixel-by-pixel basis within a dataset and reliably quantifies orientation even for locations not centered on a feature ridge, which is used here to derive a quasi-continuous map of the chromospheric fine-structure projection angle. For time-series analysis, a procedure is developed that uses a hierarchical application of the RHT to automatically derive the apparent motion of coronal rain observed off-limb. Essential to the success of this technique is the formulation presented in this article for the RHT error analysis as it provides a means to properly filter results.
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Notes
The term “axis” signifies that the parallel and antiparallel directions are treated equivalently, meaning that a given direction is sampled along a line extending through the origin and between two points on the kernel edge. Thus, the data measures non-vectorial undirected lines.
See the definition of ridge in Section 1.
The factor of 0.5 in introduced by the back-transformation into axial coordinates.
\(\alpha\) is the canonically defined significance/confidence level.
It may be possible to fit a model to multimodal distributions such that intersecting lines may be individually identified, as was done for bird migratory patterns by Ożarowska et al. (2013). Explicit fitting of the distribution, however, is numerically slow and not considered here.
Downloaded from http://www.lmsal.com/~aschwand/software/tracing/tracing_tutorial1.html on 9 June 2017.
Perceptually uniform color tables, as described and generated by Kovesi (2015), are used to represent the cyclical projected orientation angles.
Primarily single-core operations within the Interactive Data Language (IDL®) were used on a 3.3 GHz processor accessing DDR4 memory. IDL® is a product of Exelis Visual Information Solutions, Inc., a subsidiary of Harris Corporation (Exelis VIS).
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Acknowledgements
The National Solar Observatory (NSO) is operated by the Association of Universities for Research in Astronomy, Inc. (AURA), under cooperative agreement with the National Science Foundation. IRIS is a NASA Small Explorer Mission developed and operated by LMSAL with mission operations executed at NASA Ames Research center and major contributions to downlink communications funded by ESA and the Norwegian Space Centre. The author is grateful to Kevin Reardon for providing the IBIS dataset as well as for a careful reading of the manuscript.
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Schad, T. Automated Spatiotemporal Analysis of Fibrils and Coronal Rain Using the Rolling Hough Transform. Sol Phys 292, 132 (2017). https://doi.org/10.1007/s11207-017-1153-9
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DOI: https://doi.org/10.1007/s11207-017-1153-9