Stripe Noise Elimination with a Novel Trend Repair Method for Push-Broom Thermal Images
Abstract
:1. Introduction
2. Experimental Data
2.1. GF5-02 VIMI Data
2.2. Landsat 8 TIRS Data
3. Methodology
3.1. Histogram Matching Method
- (1)
- Calculate the probability density function (PDF) of each column and the entire image:
- (2)
- Calculate the CPDF of each PDF histogram:
- (3)
- Match each column CPDF curve to the entire image CPDF curve to obtain a lookup table:
3.2. Trend Repair Method
- (1)
- Find two nearest adjacent normal columns: find two adjacent normal columns located on both sides of the defective column; record the distances (dis1 and dis2) to the defective column separately.
- (2)
- Calculate the mean and standard deviation columns: combine one of two adjacent normal columns with the defective column and define a 2 × 2 window. Slide the window and calculate its mean and standard deviation iteratively. The mean column (MC) and standard deviation column (SC) are obtained at the end of the ergodic.
- (3)
- Classify within two columns: contrast the first mean value in the mean column with the subsequent mean values and compare each pair of contiguous standard deviation values in the standard deviation column acquired from previous steps in sequence. If the difference values are less than the preset thresholds TMC and TSC (these two thresholds are automatically determined according to the mean and standard deviation columns; for the calculation method, refer to Formulas (6) and (7)) simultaneously, then label the corresponding 2 × 2 pixels in the same class; otherwise, two pixels at the end of the current window are labeled as the beginning of another class. Then, the comparison steps are repeated until the final pixel is reached. Ultimately, the two combined columns are partitioned into several distinct segments, and corresponding segments which represent class in defective column (CDC) and class in normal column (CNC) exhibit similar structural characteristics.
- (4)
- Calculate mean value: calculate mean values of the different classes in the defective column and corresponding normal column to reach classified mean columns.
- (5)
- Repeat the steps: for the other adjacent normal column, repeat steps (2) to (4).
- (6)
- Correct preliminarily: traverse every pixel in the original defective column, subtract the mean of the class to which the pixel belongs in the classified defective mean column from its initial value. Then, add the difference value to its corresponding class mean in the classified normal mean column to obtain its preliminary correction value:
- (7)
- Weight the preliminary corrected values: considering the influence of the geospatial structure of adjacent pixels on the contaminated value, calculate the final corrected value by preliminary correction values of both side pixels using the inverse distance weighting method:
- (8)
- Traverse the defective column: traverse and correct each pixel in the defective columns to reach the new corrected image.
3.3. Index of Assessment
3.3.1. Streaking Metrics
3.3.2. Structural Similarity
3.3.3. Peak Signal-to-Noise Ratio
3.3.4. Improvement Factor
4. Results
4.1. Visual Analysis
4.2. Results of the Assessment Index
4.3. Accuracy
5. Conclusions and Discussion
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix B
References
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Number | Date | Surface Type |
---|---|---|
a | 28 October 2021 | Cropland Building |
b | 28 October 2021 | Mountain |
c | 30 October 2021 | Building |
d | 1 November 2021 | Cloud Desert |
e | 9 November 2021 | Water Soil |
f | 2 November 2021 | Desert Mountain |
g | 7 November 2021 | Cloud Mountain |
h | 7 November 2021 | Desert |
i | 7 November 2021 | Water Building Soil |
j | 9 November 2021 | Cloud Vegetation |
Number | Method | Piece-Wise Method | Iterated WLS Facet Method | Trend Repair Method | |||
---|---|---|---|---|---|---|---|
Index | B9 | B10 | B9 | B10 | B9 | B10 | |
a | SSIM | 0.9376 | 0.9538 | 0.9544 | 0.9679 | 0.9616 | 0.9712 |
PSNR | 62.53 | 60.98 | 64.26 | 62.70 | 64.77 | 63.13 | |
IF | 6.19 | 11.43 | −6.64 | 2.44 | 22.84 | 28.26 | |
b | SSIM | 0.9609 | 0.9654 | 0.9703 | 0.9737 | 0.9746 | 0.9764 |
PSNR | 61.20 | 61.87 | 62.60 | 63.18 | 63.16 | 63.60 | |
IF | 4.75 | 5.33 | −2.73 | −3.63 | 16.77 | 21.00 | |
c | SSIM | 0.8615 | 0.9362 | 0.8870 | 0.9487 | 0.9119 | 0.9592 |
PSNR | 56.70 | 58.13 | 58.39 | 59.31 | 58.97 | 60.18 | |
IF | 10.90 | 14.60 | −2.83 | −4.84 | 26.65 | 27.06 | |
d | SSIM | 0.9508 | 0.9119 | 0.9602 | 0.9273 | 0.9592 | 0.9256 |
PSNR | 60.13 | 57.57 | 61.15 | 58.57 | 60.95 | 58.35 | |
IF | 4.51 | 10.21 | −1.94 | −1.93 | 22.72 | 32.11 | |
e | SSIM | 0.9039 | 0.9365 | 0.9257 | 0.9436 | 0.9294 | 0.9605 |
PSNR | 55.53 | 57.39 | 56.92 | 58.08 | 56.93 | 59.49 | |
IF | 12.59 | 12.34 | −1.32 | 9.38 | 29.14 | 21.89 | |
f | SSIM | 0.9443 | 0.8996 | 0.9469 | 0.8935 | 0.9637 | 0.9215 |
PSNR | 56.09 | 56.56 | 56.39 | 56.45 | 57.99 | 57.69 | |
IF | 3.81 | 6.35 | −2.29 | −2.49 | 20.91 | 23.30 | |
g | SSIM | 0.9558 | 0.9538 | 0.9600 | 0.9555 | 0.9742 | 0.9688 |
PSNR | 61.18 | 60.01 | 61.77 | 60.31 | 63.55 | 61.77 | |
IF | 4.23 | 8.01 | −2.32 | −2.39 | 20.04 | 27.59 | |
h | SSIM | 0.9299 | 0.9160 | 0.9216 | 0.9024 | 0.9500 | 0.9327 |
PSNR | 59.83 | 59.43 | 59.52 | 59.03 | 61.29 | 60.49 | |
IF | 5.13 | 9.76 | 1.45 | 3.56 | 20.76 | 27.68 | |
i | SSIM | 0.7782 | 0.8055 | 0.7943 | 0.8040 | 0.8505 | 0.8747 |
PSNR | 56.29 | 54.29 | 57.20 | 54.74 | 58.16 | 56.35 | |
IF | 4.86 | 12.82 | −3.22 | 5.63 | 19.37 | 29.75 | |
j | SSIM | 0.9474 | 0.9344 | 0.9497 | 0.9330 | 0.9591 | 0.9489 |
PSNR | 58.95 | 58.93 | 59.25 | 58.97 | 60.05 | 60.04 | |
IF | 3.42 | 8.29 | −1.44 | 1.28 | 17.12 | 23.02 |
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Zhang, Z.; Li, H.; Du, Y.; Chen, Y.; Zhao, G.; Bian, Z.; Cao, B.; Xiao, Q.; Liu, Q. Stripe Noise Elimination with a Novel Trend Repair Method for Push-Broom Thermal Images. Remote Sens. 2024, 16, 3299. https://doi.org/10.3390/rs16173299
Zhang Z, Li H, Du Y, Chen Y, Zhao G, Bian Z, Cao B, Xiao Q, Liu Q. Stripe Noise Elimination with a Novel Trend Repair Method for Push-Broom Thermal Images. Remote Sensing. 2024; 16(17):3299. https://doi.org/10.3390/rs16173299
Chicago/Turabian StyleZhang, Zelin, Hua Li, Yongming Du, Yao Chen, Guoxiang Zhao, Zunjian Bian, Biao Cao, Qing Xiao, and Qinhuo Liu. 2024. "Stripe Noise Elimination with a Novel Trend Repair Method for Push-Broom Thermal Images" Remote Sensing 16, no. 17: 3299. https://doi.org/10.3390/rs16173299