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Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification.

V Jatla, M S Pattichis, C N Arge

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|October 4, 2019

Summary

This summary is machine-generated.

This study developed image processing methods to select physical models for predicting geomagnetic storms using solar coronal hole data. The validated approach achieved 95.5% accuracy in classifying solar maps.

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Area of Science:

* Solar Physics
* Space Weather Prediction
* Image Processing

Background:

* Accurate physical models are crucial for predicting geomagnetic storms.
* Identifying and analyzing coronal holes in solar images is key to model selection.
* Existing methods for coronal hole segmentation and matching require improvement.

Purpose of the Study:

* To develop and validate novel image processing techniques for selecting physical models of the solar corona.
* To improve the accuracy of coronal hole segmentation and matching for enhanced geomagnetic storm prediction.
* To establish a robust framework for classifying solar physical maps based on coronal hole features.

Main Methods:

* Developed a multi-modal segmentation method combining three techniques to initialize a level-set method for precise coronal hole boundary detection.
* Introduced a Linear Programming approach for matching coronal hole clusters across different solar maps.
* Employed Random Forests for the final matching of coronal hole clusters.
* Validated methods using consensus maps, manual clustering, manual classification, and testing on 50 maps.

Main Results:

* The multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCN in boundary detection accuracy.
* Achieved a 95.5% accuracy in classifying solar physical maps.
* Demonstrated effective matching of coronal hole clusters using Linear Programming and Random Forests.

Conclusions:

* The developed image processing methods provide a significant advancement in selecting physical models for geomagnetic storm prediction.
* The multi-modal segmentation and cluster matching techniques offer high accuracy and reliability.
* This work lays the foundation for more precise space weather forecasting through improved analysis of solar imagery.