Recent advances in computing technologies have renewed interest in the intelligent systems for automatic interpretation of ionograms, images obtained by remote sensing of the ionospheric plasma. The ionogram “autoscaling” techniques based on the template matching method, previously rendered unrealistic for their computing complexity, have now become feasible. This work presents an automatic scaling technique for extracting the main features of the F1 and F2 layers of the ionosphere, such as critical frequency and virtual height, from vertical incidence ionograms that do not distinguish O/X polarization of the echoes. The proposed technique uses the quasi-parabolic segments (QPS) to model the electron density profile shapes that are used to synthesize a pool of candidate traces. Moreover, the empirical orthogonal functions and image technique are applied to reduce the size of the candidate traces so that the auto-scaling algorithm can run in realistic time. With the template matching algorithm, the technique will provide the initial parameters of the QPS model for the F1 and F2 layers, which are then fine-tuned to obtain the better fitting parameters. In order to evaluate the performance of this technique, a large data set of ionograms recorded in Wuhan at daytime and nighttime in winter, summer, and equinoctial months, are analyzed and investigated. The automatic scaling results are compared with manually scaling results. Our results indicate that the proposed technique described in this paper is reliable and efficient and will facilitate the statistical study of temporal and spatial ionospheric characteristics over Wuhan.
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