## 1 Introduction

[2] The ionosphere is an essential part of the near-Earth space environment, and it affects directly the propagation of radio waves. Ionograms generated by various ionosondes contain important information on the ionosphere. Ionogram scaling is a time-consuming and laborious task; therefore, automatic scaling procedures, widely used for handling digital ionograms, have been developed in the past [*Fox and Blundell*, 1989; *Ding et al*., 2007; *Scotto and Pezzopane*, 2007; *Pezzopane and Scotto*, 2007, 2008]. Even though considerable progress has been made in the recent years, the reliability and accuracy of the automatically scaled data have remained a challenge.

[3] The most widely known automatic scaling system is ARTIST (Automatic Real Time Ionogram Scaling with True Height Analysis), developed at the Center for Atmospheric Research, University of Massachusetts Lowell [*Huang and Reinisch*, 1982; *Reinisch and Huang*, 1982, 1983; *Galkin et al*., 1996; *Galkin and Reinisch*, 2008]. It is based on neural networks and a hyperbolic trace fitting method and needs polarization information to identify the radio wave modes. In this paper, we provide a new method which is proposed to scale *F* layer traces of ionograms and to separate the *O*-mode and *X*-mode echoes automatically. Our approach is based on image processing methods and does not need polarization information, and in this way, it seems a general approach.

[4] In this paper, we scale the ionograms in three steps. The first step is noise reduction preprocessing to obtain a clean ionogram, the second step is to extract layer traces from the ionogram, and the last step is to identify the characteristics of each layer. In our previous research [*Chen et al*., 2011, 2012; *Z. Chen et al*., Ionograms denoising via curvelet transform, submitted to *Advances in Space Research*, 2012], we have developed methods to perform the first step in eliminating noise in an ionogram. Therefore, this paper will provide only a brief description of the first step and will focus on the next two steps. The algorithm proposed here is based on mathematical morphology and graph theory. The performance of this algorithm is tested by data from an ionosonde, the Chinese Academy of Sciences Digital Ionosonde (CAS-DIS) installed at Dulan (36°17′N, 98°5′E).

[5] This paper is organized as follows. In section 2, the experimental data collected by CAS-DIS are described. Section 3 illustrates the algorithm, including the three steps mentioned above. Section 4 gives the results of this algorithm and discusses its performance.