State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere

Sensors

卷:22

Yung-Hui Li¹  Lung-Chih Tsai²  Enkhtuya Tsogtbaatar³  Yu-Ciang Lin⁴  Merlin M. Mendoza⁴  Mon-Chai Hsieh⁴  Guan-Han Huang⁴  Chia-Hsien Lin⁴  Alexei V. Dmitriev⁴  Yu-Chi Chang⁴  Hao-Wei Hsu⁴ 

[1] AI Research Center, Hon Hai Research Institute, Taipei 114699, Taiwan;

[2] Center for Space and Remote Sensing Research, National Central University, Taoyuan City 320317, Taiwan;

[3] Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan;

[4] Department of Space Science and Engineering, National Central University, Taoyuan City 320317, Taiwan;

关键词: ionospheric sounding;    artificial intelligence;    image segmentation;   

DOI  :  10.3390/s22072758

来源: DOAJ

【 摘 要 】

Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct and train five convolutional neural network (CNN) models: DeepLab, fully convolutional DenseNet24 (FC-DenseNet24), deep watershed transform (DWT), Mask R-CNN, and spatial attention-UNet (SA-UNet) for the recovery of ionograms. The performance of the models is evaluated by intersection over union (IoU). We collect and manually label 6131 ionograms, which are acquired from a low-latitude ionosonde in Taiwan. These ionograms are contaminated by strong quasi-static noise, with an average signal-to-noise ratio (SNR) equal to 1.4. Applying the five models to these noisy ionograms, we show that the models can recover useful signals with IoU > 0.6. The highest accuracy is achieved by SA-UNet. For signals with less than 15% of samples in the data set, they can be recovered by Mask R-CNN to some degree (IoU > 0.2). In addition to the number of samples, we identify and examine the effects of three factors: (1) SNR, (2) shape of signal, (3) overlapping of signals on the recovery accuracy of different models. Our results indicate that FC-DenseNet24, DWT, Mask R-CNN and SA-UNet are capable of identifying signals from very noisy ionograms (SNR < 1.4), overlapping signals can be well identified by DWT, Mask R-CNN and SA-UNet, and that more elongated signals are better identified by all models.

【 授权许可】

Unknown