Image patch analysis and clustering of sunspots: a dimensionality reduction approach
This work addresses bias in visual classification of sunspots for predicting solar activity, but it is incremental as it builds on existing dimensionality reduction techniques.
The paper tackled the problem of classifying sunspot groups by developing a new clustering approach using continuum and magnetogram images to analyze spatial and modal dependencies, resulting in a method that estimates intrinsic parameters and patterns at different scales.
Sunspots, as seen in white light or continuum images, are associated with regions of high magnetic activity on the Sun, visible on magnetogram images. Their complexity is correlated with explosive solar activity and so classifying these active regions is useful for predicting future solar activity. Current classification of sunspot groups is visually based and suffers from bias. Supervised learning methods can reduce human bias but fail to optimally capitalize on the information present in sunspot images. This paper uses two image modalities (continuum and magnetogram) to characterize the spatial and modal interactions of sunspot and magnetic active region images and presents a new approach to cluster the images. Specifically, in the framework of image patch analysis, we estimate the number of intrinsic parameters required to describe the spatial and modal dependencies, the correlation between the two modalities and the corresponding spatial patterns, and examine the phenomena at different scales within the images. To do this, we use linear and nonlinear intrinsic dimension estimators, canonical correlation analysis, and multiresolution analysis of intrinsic dimension.