CHESS: Coronal Hole Extraction with Semantic Segmentation

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This project aims at expanding the training of two Convolutional Neural Networks (CNNs) that we have already developed to obtain a more efficient, more accurate, and least-biased CNN model for segmenting coronal holes (CHs). Our two CNNs are based on (i) a U-Net and (ii) a Res-U-Net architecture for Coronal Hole Image Segmentation,with model (ii) currently being more accurate than model (i). These two CNNs have been pre-trained with the coronal hole (CH) boundary data from the Heliophysics Events Knowledgebase (HEK). These initial, pre-training data o...