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 multi-channel unsupervised spatially-constrained fuzzy clustering procedure, where the classes are determined by minimization of intra-class variance.
The description of the segmentation process in terms of fuzzy logic was motivated by the facts that information provided by a solar EUV image is uncertain (Poisson and readout noise, cosmic ray hits) and subject to both observational biases (line of sight integration of a transparent volume) and interpretation (the apparent boundary between regions is a matter of convention).
This technique attributes to every pixel a probability of belonging to a particular class (AR, QS, CH). By assigning each pixel to the class for which it has the largest probability of belonging, we get image segmentations. In order to cope successfully with intensity outlier pixels such as those affected by cosmic rays and proton storms, a spatial regularization term was integrated in the clustering algorithm. Since the solar corona is optically thin, and since the intensity in EUV images is obtained through an integration along the line of sight, there is a limb brightening effect in those images which may hinder the segmentation process. Therefore, we first process the EUV images so as to lower the enhanced brightness near the limb. The initial SPoCA class contours are automatically postprocessed using a conditional morphological opening with a circular isotropic element of size equal to one.
SPoCA has been applied successfully to a series of EIT image pairs (171 & 195 A) spanning almost a full solar cycle, see Barra et al, 2009.
Version 1.0 SPoCA is now (January 2012) running at LMSAL and produces entries to the Heliophysics Knowledge Base (HEK) related to the position and characteristics of Active Regions (SPOCA_AR) and Coronal Holes (SPOCA_CH).
Version 1.0 of SPOCA
SPOCA, HEK, and the SolarSoft
See here for explanations on how to retrieve events using SSW/IDL.
Code
- The core of SPoCA is written in C++. Some wrapper for IDL and Python are also available.
Version 1.0 of SPOCA can be downloaded from the svn repository.
Documentation is available here.
Active Region detection and tracking
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EIT Movie from 12 May-23 June 2003 with AR overlay. From the maps of e.g. ARs, connected AR pixels are gathered by way of a region growing technique. It provides the instantaneous location of the center of mass of any given AR, its area, the coordinates of the bounding box. Next, the AR are tracked over time. |
References
Fast and robust segmentation of solar EUV images: algorithm and results for solar cycle 23 Barra V., Delouille V., Kretzschmar M., Hochedez J.-F., Astronomy & Astrophysics, 505(1), 361--371, 2009
Segmentation, Tracking and Characterization of Solar Features from EIT Solar Corona Images V. Barra, V. Delouille, and J.-F. Hochedez, Lecture Notes in Computer Science, Volume 5575/2009, 199--208, 2009 pdf
Segmentation of extreme ultraviolet solar images via multichannel fuzzy clustering Barra V., Delouille V., Hochedez J.-F. Advances in Space Research, 42, 917--925, 2008.
Segmentation of EIT Images Using Fuzzy Clustering: a Preliminary Study Barra, V., Delouille, V., Hochedez, J.-F., & Chainais, P. ESA Special Publication, 600, 77.1, 2005.
