SPoCA : Spatial Possibilistic Clustering Algorithm
The Spatial Possibilistic Clustering Algorithm (SPoCA) produces a segmentation of EUV solar images into regions named here `classes' corresponding to Active Regions (AR), Coronal Holes (CH), and the Quiet Sun (QS). SPoCA uses a multichannel fuzzy- logic clustering procedure. It has been applied successfully to a series of EIT image pairs (171 & 195 A) spanning almost a full solar cycle. The classes are determined by minimization of intra-class variance. The method is generic and therefore portable to other instruments, and in particular to SDO-AIA. SPoCA involves a preprocessing by which the limb brightness discontinuity is attenuated. SPoCA can also take transformed EUV images as input, such as DEM maps obtained from AIA images (using software supplied by AIA investigators).
The level-1 product of the procedure is a set of maps giving the probability of membership to each class. Several higher level products can be generated from these maps of probability:
- A segmentation map, attributing a class to the pixel according to its highest probability of membership.
- A probability density function giving, for each pixel intensity value, the probability that this pixel belong to a particular class.
- Statistics such as area, mean intensity, and integrated intensity can be computed for each class.
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From the maps of e.g. ARs, connected AR pixels are then gathered by way of a region growing technique, see Figure 1. It provides the instantaneous location of the center of mass of any given AR, its area, the coordinates of the bounding box, and vectors of coordinates for all pixels belonging to the boundary (chain code). Fuzzy CH maps can be treated exactly as the AR maps, producing area, location of `mass center', and location of boundary for a connected element of the CH map. |
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Related Publications
- Barra V., Delouille V., Hochedez J.-F.:2008 `Segmentation of extreme ultraviolet solar images via multichannel fuzzy clustering', Advances in Space Research, 42, 917--925.
Barra V., Delouille V., Kretzschmar M., Hochedez J.-F.:2009 `Fast and robust segmentation of solar EUV images: algorithm and results for solar cycle 23', A&A Accepted
Barra, V., Delouille, V., Hochedez, J.-F., & Chainais, P.:2005 `Segmentation of EIT Images Using Fuzzy Clustering: a Preliminary Study', ESA Special Publication, 600, 77.1.
Proceedings
V. Barra, V. Delouille, and J.-F. Hochedez: 2009 `Segmentation, Tracking and Characterization of Solar Features from EIT Solar Corona Images', Lecture Notes in Computer Science, Volume 5575/2009, Pages 199-208, pdf