Automatic classification of spread‐F types in ionogram images using support vector machine and convolutional neural network
| dc.contributor.author | Benchawattananon, Phongsachot | |
| dc.contributor.author | Siritaratiwat, Apirat | |
| dc.contributor.author | Supnithi, Pornchai | |
| dc.contributor.author | Nishioka, Michi | |
| dc.contributor.author | Perwitasari, Septi | |
| dc.contributor.author | Suthisopapan, Puripong | |
| dc.contributor.author | Kruesubthaworn, Anan | |
| dc.date.accessioned | 2026-08-06T10:47:51Z | |
| dc.date.available | 2026-08-06T10:47:51Z | |
| dc.date.issued | 2024-12-01 | |
| dc.description.abstract | An ionogram image serves as a valuable data for examining the ionospheric bottom side characteristics and variabilities. Spread-F is indicated or identified by plasma irregularity in the ionospheric region. Diffused echo in the ionogram images particularly pose challenges for efficient interpretation required in further applications. An automatic classification of spread-F is presented in this study. Ionogram images are automatically classified using preprocessing techniques to improve the classification performance. In this study, the classification is designed by two machine learning algorithms, including support vector machine (SVM) and convolutional neural network (CNN). The CNN model with preprocessing technique outperforms the SVM alternative based on 4,692 labelled ionogram images from the FMCW-type ionosonde at Chumphon station, Thailand. The model successfully classified clear, frequency spread-F (FSF), range spread-F (RSF), strong spread-F (SSF), and unidentified class with an accuracy of 98.0%, 85.1%, 90.7%, 66.7%, and 99.2%, respectively. The proposed automatic classification models achieved to classify classes of ionogram images. In addition, the image filtering and data preprocessing are useful with ionogram images for improving the model classification performance. Graphical Abstract: (Figure presented.) | |
| dc.identifier.citation | Earth Planets and Space, 76(1), 2024 | |
| dc.identifier.doi | 10.1186/s40623-024-02002-x | |
| dc.identifier.issn | 13438832 | |
| dc.identifier.other | 2-s2.0-85190114732 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/16100 | |
| dc.source | Earth Planets and Space | |
| dc.subject | Convolution neural network | |
| dc.subject | Image classification | |
| dc.subject | Image preprocessing | |
| dc.subject | Ionogram image | |
| dc.subject | Spread-F | |
| dc.subject | Support vector machine | |
| dc.title | Automatic classification of spread‐F types in ionogram images using support vector machine and convolutional neural network | |
| dc.type | Article |