1. Bayesian Feature Recognition for NSO Multidimensional Imagery Harrison P. Jones (NSO), Judit M. Pap (GEST,UMBC; NASA’s GSFC), and Michael J. Turmon (JPL) Scope 3-Year Project to: Develop Bayesian image segmentation (feature identification) methods for multidimensional imagery from the NSO/Spectromagnetograph (SPM) and NSO/SOLIS Vector Spectromagnetograph (VSM). Classify all SPM and VSM-to-date magnetograms. Compare feature records with solar irradiance variations, solar activity, and both models and observations of the solar cycle. Explore application to future high-volume data sets such as HMI. Tasks (progress in first six months shown in red). Select and preprocess training and test sets of images Develop algorithms for Class-Conditional Probabilities (Bayesian Prior) Gaussian Mixture Models Direct Histogram Interpolation Use available feature classifications (e.g., Harvey and White, 1999, Astrophys J. 515, 812) to train and test the methods. Apply to all SPM data Adapt to VSM line-of-sight magnetograms Extend to vector magnetograms Apply to available VSM data Compare with irradiance and other solar observations and models. ContinuumMagnetogram Harvey-White Segmentation Gaussian Mixture Segmentation Early Test Results 04 Nov 1998 05 Nov 1998 06 Nov 1998 Network = dark blue; Enhanced Network = aqua; Active Region = yellow; Decaying Region = red; Sunspot = orange Histogram Interpolation Segmentation Progress Image Selection Have chosen years 1996-1998 for initial work; Harvey-White and MDI image segmentations are available. Have selected a training set of SPM images from this period. Have selected a preliminary test set of 37 images from this period. MDI magnetograms available close to time of observation SPM image quality is good. Have improved removal of center-to-limb variation. Harvey-White segmentation chosen for initial development since it is specific to SPM data and requires no cross-image registration. Class-conditional probability algorithms Gaussian Mixture Models Have extended Turmon et al. models to include equivalent width in SPM data and adapted to scaling of SPM data. Have used subsets of scatterplots of Harvey-White segmentation of test set to compute preliminary probability models for all days in test set. Histogram Interpolation Have computed scaled (quasi log-log) 3D histograms for Harvey- White segmentation of test set. Have developed interpolation algorithm (tri-cubic near histogram means, piecewise linear elsewhere) and computed probability densities for each feature class for three days shown to right. Image Segmentation First results for both mixture and histogram interpolation models are compared in image panel to right with original data and Harvey- White segmentation. Discussion Preliminary computational procedures to carry out the testing and analysis for the project are working—no showstoppers yet. All three segmentation methods give similar results for sunspots (no surprise). The Harvey-White segmentations are large scale, while the two variants of the Bayesian segmentation show much more fine structure. The structure of the Sun is inherently multiscale. We suspect that the large scale of the Harvey-White features may be more useful for solar cycle studies while comparison with irradiance variations may be more effective with finer scale features. The smoothness of the Bayesian techniques can be “tuned” in computing the global probability over the entire image of a given segmentation. We have not widely exploited this capability in the preliminary work. The Bayesian methods do not reliably reproduce the Harvey-White decaying regions. The probability densities (histograms) for decaying regions are very similar to active regions and are not too different from enhanced network; more prior information may be needed (see below). The balance between Harvey-White “network” (basically quiet Sun) and “enhanced network” is delicate and seems to be noisier using histogram interpolation. This may imply that smoother interpolation schemes are required. Work for the near future. Explore smoothing techniques. Adjust details such as parameters of the Gaussian mixture models, binning of histograms, and interpolation bases. Consider methods for improving prior information. Add unipolarity, line depth, and perhaps some measure of history. Try principal components or factor analysis to keep dimensionality manageable while producing more distinct histograms and mixture models for the various feature classes Extend to larger image sets. Test against other known image segmentation schemes.