Image patch analysis and clustering of sunspots: a dimensionality reduction approach

TL;DR

This paper introduces a novel dimensionality reduction approach for analyzing and clustering sunspot images from multiple modalities, aiming to improve understanding of their complex structures and interactions.

Contribution

It presents a new method combining intrinsic dimension estimation and canonical correlation analysis to characterize sunspot images across different scales and modalities.

Findings

Identified the number of intrinsic parameters needed to describe sunspot images.

Revealed spatial and modal dependencies between continuum and magnetogram images.

Provided insights into the multiscale structure of sunspot regions.

Abstract

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.