  We train a deep learning artificial neural network model, Spatial Attention
U-Net to recover useful ionospheric signals from noisy ionogram data measured
by Hualien's Vertical Incidence Pulsed Ionospheric Radar. Our results show that
the model can well identify F2 layer ordinary and extraordinary modes (F2o,
F2x) and the combined signals of the E layer (ordinary and extraordinary modes
and sporadic Es). The model is also capable of identifying some signals that
were not labeled. The performance of the model can be significantly degraded by
insufficient number of samples in the data set. From the recovered signals, we
determine the critical frequencies of F2o and F2x and the intersection
frequency between the two signals. The difference between the two critical
frequencies is peaking at 0.63 MHz, with the uncertainty being 0.18 MHz.
