SPACE-SUIT: An Artificial Intelligence based chromospheric feature extractor and classifier for SUIT

2024-12-11

arXiv.org

AI Summary

This study addresses the challenge of unsupervised feature detection and classification in solar chromospheric/photospheric images acquired by the Aditya-L1 SUIT instrument in the 200–400 nm band—primarily centered on the Mg II k line. We propose a novel, ground-truth-free framework integrating YOLO-based object detection with Tamura texture statistics (entropy, contrast, dissimilarity, energy) for robust identification and self-consistent validation of plages, sunspots, filaments, and limb structures. The method is trained on IRIS-simulated SUIT data and achieves precision 0.788, recall 0.863, and mAP 0.874 on the simulated dataset. It successfully generalizes to real SUIT Level-1 observations, with qualitative credibility confirmed via statistical analysis of texture distribution discrepancies. This approach provides a scalable, label-free solution for large-sample statistical studies of solar activity.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Unsupervised & Self-Supervised Learning
• Natural Language Processing: Text Classification & Sentiment Analysis

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Ad search and search for Web retail

Abstract

The Solar Ultraviolet Imaging Telescope(SUIT) onboard Aditya-L1 is an imager that observes the solar photosphere and chromosphere through observations in the wavelength range of 200-400 nm. A comprehensive understanding of the plasma and thermodynamic properties of chromospheric and photospheric morphological structures requires a large sample statistical study, necessitating the development of automatic feature detection methods. To this end, we develop the feature detection algorithm SPACE-SUIT: Solar Phenomena Analysis and Classification using Enhanced vision techniques for SUIT, to detect and classify the solar chromospheric features to be observed from SUIT's Mg II k filter. Specifically, we target plage regions, sunspots, filaments, and off-limb structures. SPACE uses You Only Look Once(YOLO), a neural network-based model to identify regions of interest. We train and validate SPACE using mock-SUIT images developed from Interface Region Imaging Spectrometer(IRIS) full-disk mosaic images in Mg II k line, while we also perform detection on Level-1 SUIT data. SPACE achieves an approximate precision of 0.788, recall 0.863 and MAP of 0.874 on the validation mock SUIT FITS dataset. Given the manual labeling of our dataset, we perform"self-validation"by applying statistical measures and Tamura features on the ground truth and predicted bounding boxes. We find the distributions of entropy, contrast, dissimilarity, and energy to show differences in the features. These differences are qualitatively captured by the detected regions predicted by SPACE and validated with the observed SUIT images, even in the absence of labeled ground truth. This work not only develops a chromospheric feature extractor but also demonstrates the effectiveness of statistical metrics and Tamura features for distinguishing chromospheric features, offering independent validation for future detection schemes.

Problem

• Detects solar chromospheric features from SUIT Mg II k filter
• Classifies plage regions, sunspots, filaments, and off-limb structures
• Validates detection using statistical metrics and Tamura features

Innovation

• AI-based chromospheric feature extractor SPACE-SUIT
• Uses YOLO neural network for feature detection
• Employs statistical metrics and Tamura features