Inversion of Wind and Temperature from Low SNR FPI Interferograms
Abstract
:1. Introduction
2. FPI Model
2.1. Forward Model
2.2. Inversion Algorithm
- (1)
- Binarization. The method thresholds the interferogram and fits rings to obtain the center, as shown in Figure 2a. The median value of all pixels in the image is taken as the threshold. Multiple rings can be fitted simultaneously to improve the stability.
- (2)
- Peak fitting. First, the raw images are filtered using a median filter. [17] Then, the approximate location of the center is determined manually, and a row of pixels is taken out near it to fit each peak, as shown in Figure 2b. The mean of the two peaks of the same order ring is the horizontal coordinate of the center. Similarly, the vertical coordinates can also be obtained. If only one row or column is used to determine the center position, the results will be biased easily due to noise. We used 21 rows and columns in the actual calculation.
3. Effect of the Center Errors on Wind and Temperature Inversions
4. A New Algorithm for Determining the Center
4.1. Analysis Process
4.2. The MSDM Performance on Noisy and Distortion Interferograms
4.2.1. Gaussian White Noise
4.2.2. Poisson Noise
4.3. Distortion Interferograms
5. Application to Wind and Temperature Inversion from Real Airglow Interferograms
6. Summary
- For Poisson noise, the center deviation of the MSDM is less than 0.05 pixels. The SNR of the images has a great influence on binarization and peak fitting, so more accurate results can only be obtained from high-quality images. Especially for temperature, the MSDM results are in the range of 500 to 750 K.
- For Poisson noise, the performance of the MSDM maintains very good performance for different signal intensities. However, binarization and peak fitting are more affected by noise.
- For distortion interferograms, the binarization method calculates completely wrong centers, while the MSDM and peak fitting give more accurate results.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Wei, Y.; Gu, S.-Y.; Yang, Z.; Huang, C.; Li, N.; Hu, G.; Dou, X. Inversion of Wind and Temperature from Low SNR FPI Interferograms. Remote Sens. 2023, 15, 1934. https://doi.org/10.3390/rs15071934
Wei Y, Gu S-Y, Yang Z, Huang C, Li N, Hu G, Dou X. Inversion of Wind and Temperature from Low SNR FPI Interferograms. Remote Sensing. 2023; 15(7):1934. https://doi.org/10.3390/rs15071934
Chicago/Turabian StyleWei, Yafei, Sheng-Yang Gu, Zhenlin Yang, Cong Huang, Na Li, Guoyuan Hu, and Xiankang Dou. 2023. "Inversion of Wind and Temperature from Low SNR FPI Interferograms" Remote Sensing 15, no. 7: 1934. https://doi.org/10.3390/rs15071934
APA StyleWei, Y., Gu, S. -Y., Yang, Z., Huang, C., Li, N., Hu, G., & Dou, X. (2023). Inversion of Wind and Temperature from Low SNR FPI Interferograms. Remote Sensing, 15(7), 1934. https://doi.org/10.3390/rs15071934