Automatic SubPixel CoRegistration of Remote Sensing Images Using Phase Correlation and Harris Detector
Abstract
:1. Introduction
2. Brief Overview of Related Work
2.1. Hybrid Registration Approach
2.2. Fine Registration Using Phase Correlation
3. Materials and Methods
3.1. Harris Corners Extraction
3.2. Point Correspondence Using Fourier Phase Matching
3.3. SubPixel Translation Estimation
3.3.1. Nelder–Mead (NM) Optimization
3.3.2. The TwoPoint Step Size (TPSS) Gradient
3.4. Detection of Outliers
Algorithm 1: RANSAC Algorithm 

3.5. Transformation Model
3.6. Workflow of the Proposed Approach
 Extract Harris corners from the sensed and reference images for each of the nine subregions.
 For each point PC_{i} in the reference image, weigh the reference template image ${p}_{1}\left[x,y\right]$ and the candidate template image ${p}_{2}\left[x,y\right]$ for each of the knearest neighbors in the sensed image by a Blackman window.
 Compute the discrete Fourier transform ${\widehat{p}}_{k}$ of each filtered image ${p}_{\mathrm{k}}\left[x,y\right]$.
 Compute the normalized crosscorrelation $R\left[k,l\right]$ and POC function $r\left[\u2206\mathrm{x},\u2206\mathrm{y}\right]$ between ${\widehat{p}}_{1}\mathrm{and}{\widehat{p}}_{2}$.
 The candidate points with the maximum magnitude of the phaseonly correlation are considered as the exact corresponding points.
 Eliminate the pairs for which the score is less than 0.3, in addition to the manytoone match with the minimum score.
 Deal with outliers using the RANSAC algorithm.
 Compute the displacement $\left(\u2206{\mathrm{x}}_{0},\u2206{\mathrm{y}}_{0}\right)$. for each corresponding point pairs using phase correlation.
 Using $\u2206{\mathrm{x}}_{0}\mathrm{and}\u2206{\mathrm{y}}_{0}$ as initial approximations, two optimization algorithms are used to find $\left(\u2206\mathrm{x},\u2206\mathrm{y}\right)$ that maximize the POC function $r\left[\u2206\mathrm{x},\u2206\mathrm{y}\right]$.
 Compute the parameters of the model transformation using least squares minimization.
 Apply model transformations to align the sensed image with the reference image.
3.7. Evaluation Criteria
4. Results and Discussion
4.1. Descriptions of Experimental Data
4.2. LargeScale Displacements Estimation with PixelLevel Accuracy
4.2.1. Effect of Two Window Functions on the Correlation Measures
4.2.2. Performance of Phase Matching
4.3. Enhanced SubPixel Displacement Estimation
4.4. Validation of the Proposed Registration Method
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Data Source  Image Size (pixels)  Year  Resolution (m)  Incidence Angles (°)  Angle of Solar Elevation (°)  Solar Azimuth (°) 

Pleiades sensed image  25,855 × 38,808  2014  0.5  –17  30  160 
Pleiades reference image  37,430 × 42,068  2013  0.5  18  31  161 
Data Source  Image Size (pixels)  Date  Resolution (m)  Echelle  Camera 

Aerial images  5121 × 2897  2009    1/7500  RMK TOP 15 
10,000 × 10,000  2014  0.20    ADS80  
11,271 × 10,727  2016  0.20    ADS80 
Window Function  $\mathbf{RMSE}\mathbf{of}\mathbf{\u2206}\mathbf{x}$  $\mathbf{RMSE}\mathbf{of}\mathbf{\u2206}\mathbf{y}$  Number of Wrong Matches  Number of Correct Matches  True Matching Rate 

Estimates BW  0.486  0.599  22  88  0.800 
Estimates HW  0.418  0.586  29  81  0.734 
Estimates BW and HW  0.405  0.596  29  81  0.736 
Estimates HW and BW  0.430  0.594  28  82  0.745 
Window Function  Estimates PM Only  Estimates PM Using Harris Corners  

RMSE_{x}  RMSE_{y}  RMSE_{x}  RMSE_{y}  
EstimatesBW  2.752  4.301  0.486  0.599 
EstimatesHW  2.439  4.180  0.418  0.586 
EstimatesBWHW  2.626  4.270  0.405  0.596 
EstimatesHWBW  2.486  4.278  0.430  0.594 
Methods  Found Corners Pairs  Filter Score < 0.3 and Identical Pairs  Eliminate Outliers (RANSAC)  True Matching Rate  Average Running Time (mn)  

(1)  (2)  (1)  (2)  (1)  (2)  (1)  (2)  (1)  (2)  
SURF based matching  640  19,957      13  3029  0.020  0.152  0.7  11.2 
Harris corners with SURF descriptor  574  8760      11  813  0.019  0.093  0.4  10.8 
Our Approach (MinQuality = 0.01)  30,068  806,806  6042  307,301  619  33,565  0.102  0.109  1.7  237.5 
Our Approach (MinQuality = 0.005)  35,274  1,412,805  9115  567,096  549  69,678  0.060  0.123  3.6  507.0 
Estimates Harris  Estimates TPSS  Estimates MN  

Aerial 2014–2009  RMSE_{x}  0.557  0.577  0.557 
RMSE_{y}  0.821  0.852  0.821  
Aerial 2014–2016  RMSE_{x}  0.003  0.047  0.003 
RMSE_{y}  0.007  0.192  0.007  
Satellite 2013–2014  RMSE_{x}  0.676  0.686  0.676 
RMSE_{y}  0.717  0.686  0.717 
RMSE_{x}  RMSE_{y}  

(1)  (2)  (3)  (1)  (2)  (3)  
Sensed image  3.946  1.006  0.968  6.301  1.724  1.968 
Registered image with TPS  0.252  0.062  0.428  0.373  0.084  0.402 
Registered image with firstorder polynomial transformation  0.144  0.066  0.307  0.244  0.069  0.311 
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Rasmy, L.; Sebari, I.; Ettarid, M. Automatic SubPixel CoRegistration of Remote Sensing Images Using Phase Correlation and Harris Detector. Remote Sens. 2021, 13, 2314. https://doi.org/10.3390/rs13122314
Rasmy L, Sebari I, Ettarid M. Automatic SubPixel CoRegistration of Remote Sensing Images Using Phase Correlation and Harris Detector. Remote Sensing. 2021; 13(12):2314. https://doi.org/10.3390/rs13122314
Chicago/Turabian StyleRasmy, Laila, Imane Sebari, and Mohamed Ettarid. 2021. "Automatic SubPixel CoRegistration of Remote Sensing Images Using Phase Correlation and Harris Detector" Remote Sensing 13, no. 12: 2314. https://doi.org/10.3390/rs13122314