An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery
"> Figure 1
<p>Cape Town, South Africa, test data showing (<b>a</b>) Landsat-8-L1T sensed 22 November 2015 (week 47); (<b>b</b>) Sentinel-2A L1C sensed 8 December 2015 (week 49); and (<b>c</b>) Sentinel-2A L1C sensed 18 December 2015 (week 51). The NIR (Sentinel-2: 842 nm and Landsat-8 864 nm band) TOA reflectance for each image is shown, which was reprojected to 10 m global WELD tile hh19vv12.h3v2 (sinusoidal projection, 15885 × 15885 10 m pixels).</p> "> Figure 2
<p>Limpopo Province, South Africa, test data showing (<b>a</b>) Landsat-8-L1T sensed 5 December 2015 (week 49); (<b>b</b>) Sentinel-2A L1C sensed 9 December 2015 (week 49). The NIR TOA reflectance for each image is shown, which was reprojected to 10 m global WELD tile hh20vv11.h4v3 (sinusoidal projection, 15885 × 15885 10 m pixels).</p> "> Figure 3
<p>Automated workflow to register Landsat-8 OLI to Sentinel-2A MSI WELD tiles.</p> "> Figure 4
<p>Illustration of depth-first LSM mismatch detection on the four-level image pyramid (shown for the Sentinel-2A image only).</p> "> Figure 5
<p>Illustration of the tie-points and misregistration vectors obtained from the Cape Town data (<a href="#remotesensing-08-00520-f001" class="html-fig">Figure 1</a>). The 116 green vectors point from the Landsat-8 week 47 image tie-point locations to the corresponding Sentinel-2A week 49 tie point locations. The 797 red vectors point from the Landsat-8 week 47 image tie-point locations to the corresponding Sentinel-2A week 51 tie-point locations. The vector lengths are enlarged by 80 times for visual clarity. To provide geographic context, the background image shows the Landsat-8 week 47 30 m true color image.</p> "> Figure 6
<p>False color images illustrating Landsat-8 week 47 and Sentinel-2A week 51 images (<b>a</b>) before registration and (<b>b</b>) after registration. The Sentinel NIR data are shown as red and the Landsat NIR data are shown as blue and green. A 350 × 350 30 m pixel subset over Saldanha Bay, Cape Town (northern side of the WELD tile, <a href="#remotesensing-08-00520-f001" class="html-fig">Figure 1</a>) is shown. The registration was undertaken using the affine transformation coefficients (<a href="#remotesensing-08-00520-t003" class="html-table">Table 3</a>).</p> "> Figure 7
<p>Dense-matching prediction-error maps for the translation (<b>a</b>); affine (<b>b</b>) and second order polynomial (<b>c</b>) transformations between the Cape Town Landsat-8 week 47 and Sentinel-2A week 51 image pair. Dense-matching grid points were sampled every six 10 m pixel across the 15885 × 15885 10 m WELD tile, generating 2647 × 2647 prediction-error maps (Equation (7)). Locations where there are no matches are shown as black.</p> "> Figure 8
<p>Illustration of the tie-points and misregistration vectors obtained from the Limpopo data (<a href="#remotesensing-08-00520-f002" class="html-fig">Figure 2</a>). The vectors point from the Landsat-8 week 49 image tie-point locations to the Sentinel-2A week 49 (180 red vectors) tie-point locations. The vector lengths are enlarged by 200 times for visual clarity. To provide geographic context the background image shows the Landsat-8 week 49 30 m true color image.</p> "> Figure 9
<p>Dense-matching prediction-error maps for the translation (<b>a</b>); affine (<b>b</b>); and second order polynomial (<b>c</b>) transformations, between the Limpopo Landsat-8 week 49 and Sentinel-2A week 49 image pair.</p> "> Figure 10
<p>Dense-matching maps x and y axis shifts (units 10 m pixels) between the Limpopo Landsat-8 week 49 and Sentinel-2A week 49 image pair. The affine transformation was used to guide the dense matching (<a href="#sec4dot4-remotesensing-08-00520" class="html-sec">Section 4.4</a>). The (<b>a</b>) x-shift <math display="inline"> <semantics> <mrow> <msub> <mi>x</mi> <mrow> <mi>S</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mo>−</mo> <msub> <mi>x</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> and (<b>b</b>) y-shift <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mo>−</mo> <msub> <mi>y</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> </semantics> </math> are shown, where (<math display="inline"> <semantics> <mrow> <msub> <mi>x</mi> <mrow> <mi>S</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mtext> </mtext> <mo>,</mo> <msub> <mi>y</mi> <mrow> <mi>S</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> </semantics> </math>) is the Sentinel grid-point location and (<math display="inline"> <semantics> <mrow> <msub> <mi>x</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mtext> </mtext> <mo>,</mo> <msub> <mi>y</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> </semantics> </math>) is the corresponding least squares matched Landsat location for grid-point <span class="html-italic">i</span>. Locations where there are no matches are shown as black. Note that (<math display="inline"> <semantics> <mrow> <msub> <mi>x</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mtext> </mtext> <mo>,</mo> <msub> <mi>y</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> </semantics> </math>) is theoretically independent on the transformation type and so the translation and polynomial-based shift maps, which were very similar to the affine-based shift maps, are not shown; (<b>c</b>) shows the Sentinel-2A L1C tile and detector boundaries (red) and (<b>d</b>) shows the Landsat-8 L1T image boundaries (blue).</p> ">
Abstract
:1. Introduction
2. Overview of Landsat-8 L1T and Sentinel-2A L1C Geometric Data Structure, the Common Map Projection, and the Study Test Data
2.1. Landsat-8 L1T and Sentinel-2A L1C Geometric Data Structure
2.2. Map Projection Used for Registration
2.3. Test Data
3. Registration Method
3.1. Overview
3.2. Image Pyramid Construction
3.3. Coarse Resolution 120 m Feature Point Detection
3.4. Least-Squares Area Based Image Matching
3.5. Depth-First Mismatch Detection
3.6. Transformation Coefficient Fitting
3.7. Image Registration
4. Registration Assessment
4.1. Tie-Point Misregistration Assessment
4.2. Transformation Coefficient Fitting Assessment
4.3. Qualitative Visual Registration Assessment
4.4. Dense Grid-Point Matching Registration Assessment
5. Results
5.1. Cape Town
5.1.1. Tie-Point Misregistration Assessment
5.1.2. Transformation Coefficient Fitting Assessment
5.1.3. Qualitative Visual Registration Assessment
5.1.4. Dense-Matching Prediction-Error Assessment
5.2. S.W. Limpopo
5.2.1. Tie-Point Misregistration Assessment
5.2.2. Transformation Coefficient Fitting Assessment
5.2.3. Dense-Matching Prediction-Error Assessment
6. Discussion
7. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Roy, D.P.; Wulder, M.A.; Loveland, T.R.; Woodcock, C.E.; Allen, R.G.; Anderson, M.C.; Helder, D.; Irons, J.R.; Johnson, D.M.; Kennedy, R.; et al. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 2014, 145, 154–172. [Google Scholar] [CrossRef]
- Irons, J.R.; Dwyer, J.L.; Barsi, J.A. The next Landsat satellite: The Landsat data continuity mission. Remote Sens. Environ. 2012, 122, 11–21. [Google Scholar] [CrossRef]
- Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
- European Space Agency (ESA). Sentinel-2 User Handbook, Issue 1, Rev 2, Revision 2; ESA Standard Document; ESA: Paris, France, 2015. [Google Scholar]
- European Space Agency (ESA). Sentinel-2 Products Specification Document, Issue 13.1; ESA REF: S2-PDGS-TAS-DI-PSD; ESA: Paris, France, 2015. [Google Scholar]
- Arvidson, T.; Gasch, J.; Goward, S.N. Landsat 7’s long-term acquisition plan—An innovative approach to building a global imagery archive. Remote Sens. Environ. 2001, 78, 13–26. [Google Scholar] [CrossRef]
- Loveland, T.R.; Dwyer, J.L. Landsat: Building a strong future. Remote Sens. Environ. 2012, 122, 22–29. [Google Scholar] [CrossRef]
- Languille, F.; Déchoz, C.; Gaudel, A.; Greslou, D.; de Lussy, F.; Trémas, T.; Poulain, V. Sentinel-2 geometric image quality commissioning: First results. Proc. SPIE 2015. [Google Scholar] [CrossRef]
- Storey, J.; Choate, M.; Lee, K. Landsat-8 Operational Land Imager on-orbit geometric calibration and performance. Remote Sens. 2014, 6, 11127–11152. [Google Scholar] [CrossRef]
- Storey, J. Sentinel-2 On-orbit geometric analysis and harmonization plans. In Proceedings of the Landsat Science Team Meeting, Virginia Tech, Blacksburg, VA, USA, 12–14 January 2016.
- Tucker, C.J.; Grant, D.M.; Dykstra, J.D. NASA’s global orthorectified Landsat data set. Photogramm. Eng. Remote Sens. 2004, 70, 313–322. [Google Scholar] [CrossRef]
- Gutman, G.; Huang, C.; Chander, G.; Noojipady, P.; Masek, J.G. Assessment of the NASA–USGS global land survey (GLS) datasets. Remote Sens. Environ. 2013, 134, 249–265. [Google Scholar] [CrossRef]
- Briottet, X.; Lier, P.; Valorge, C. Satellite Imagery, From Acquisition Principles to Processing of Optical Images for Observing the Sarth; Cepadues Editions: Toulouse, France, 2012. [Google Scholar]
- Dechoz, C.; Poulain, V.; Massera, S.; Languille, F.; Greslou, D.; de Lussy, F.; Gaudel, A.; L’Helguen, C.; Picard, C.; Trémas, T. Sentinel-2 global reference image. Proc. SPIE 2015. [Google Scholar] [CrossRef]
- Storey, J.C.; Roy, D.P.; Masek, J.G.; Dwyer, J.L.; Gascon, F.; Choate, M.J. A note on the temporary mis-registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery. Remote Sens. Environ. Submitted.
- Zitova, B.; Flusser, J. Image registration methods: A survey. Image Vis. Comput. 2003, 21, 977–1000. [Google Scholar] [CrossRef]
- Long, T.; Jiao, W.; He, G.; Zhang, Z. A fast and reliable matching method for automated georeferencing of remotely-sensed imagery. Remote Sens. 2016, 8, 56. [Google Scholar] [CrossRef]
- Goshtasby, A. Piecewise cubic mapping functions for image registration. Pattern Recognit. 1987, 20, 525–533. [Google Scholar] [CrossRef]
- Roy, D.P.; Devereux, B.; Grainger, B.; White, S. Parametric geometric correction of airborne thematic mapper imagery. Int. J. Remote Sens. 1997, 18, 1865–1887. [Google Scholar] [CrossRef]
- Liu, D.; Gong, P.; Kelly, M.; Guo, Q. Automatic registration of airborne images with complex local distortion. Photogramm. Eng. Remote Sens. 2006, 72, 1049–1059. [Google Scholar] [CrossRef]
- Li, R.; Hwangbo, J.; Chen, Y.; Di, K. Rigorous photogrammetric processing of HiRISE stereo imagery for Mars topographic mapping. IEEE Trans. Geosci. Remote Sens. 2011, 49, 2558–2572. [Google Scholar]
- Ustin, S.L.; Roberts, D.A.; Pinzon, J.; Jacquemoud, S.; Gardner, M.; Scheer, G.; Castaneda, C.M.; Palacios-Orueta, A. Estimating canopy water content of chaparral shrubs using optical methods. Remote Sens. Environ. 1998, 65, 280–291. [Google Scholar] [CrossRef]
- Roy, D.P.; Qin, Y.; Kovalskyy, V.; Vermote, E.F.; Ju, J.; Egorov, A.; Hansen, M.C.; Kommareddy, I.; Yan, L. Conterminous United States demonstration and characterization of MODIS-based Landsat ETM+ atmospheric correction. Remote Sens. Environ. 2014, 140, 433–449. [Google Scholar] [CrossRef]
- Gao, F.; Masek, J.G.; Wolfe, R.F. An automated registration and orthorectification package for Landsat and Landsat-like data processing. J. Appl. Remote Sens. 2009, 3, 033515. [Google Scholar]
- Devaraj, C.; Shah, C.A. Automated geometric correction of multispectral images from High Resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B. ISPRS J. Photogramm. Remote Sens. 2014, 89, 13–24. [Google Scholar] [CrossRef]
- Zhang, H.K.; Roy, D.P. Computationally inexpensive Landsat-8 Operational Land Imager (OLI) pansharpening. Remote Sens. 2016, 8, 180. [Google Scholar] [CrossRef]
- Snyder, J.P. Flattening the Earth: Two Thousand Years of Map Projections; The University of Chicago Press: Chicago, IL, USA; London, UK, 1993. [Google Scholar]
- Roy, D.P.; Ju, J.; Kline, K.; Scaramuzza, P.L.; Kovalskyy, V.; Hansen, M.; Loveland, T.R.; Vermote, E.; Zhang, C. Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sens. Environ. 2010, 114, 35–49. [Google Scholar] [CrossRef]
- Web-Enabled Landsat Data (WELD). Available online: http://globalweld.cr.usgs.gov/collections (accessed on 6 June 2016).
- Wolfe, R.; Nishihama, M.; Fleig, A.; Kuyper, J.; Roy, D.; Storey, J.; Patt, F. Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sens. Environ. 2002, 83, 31–49. [Google Scholar] [CrossRef]
- Konecny, G. Mathematical models and procedures for the geometric registration of remote sensing imagery. Int. Arch. Photogramm. Remote Sens. 1976, 21, 1–33. [Google Scholar]
- Roy, D.P.; Borak, J.; Devadiga, S.; Wolfe, R.; Zheng, M.; Descloitres, J. The MODIS land product quality assessment approach. Remote Sens. Environ. 2002, 83, 62–76. [Google Scholar] [CrossRef]
- Shlien, S. Geometric correction, registration and resampling of Landsat imagery. Can. J. Remote Sens. 1979, 5, 75–89. [Google Scholar] [CrossRef]
- Thevenaz, P.; Ruttimann, U.E.; Unser, M. A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 1998, 7, 27–41. [Google Scholar] [CrossRef] [PubMed]
- Adelson, E.H.; Anderson, C.H.; Bergen, J.R.; Burt, P.J.; Ogden, J.M. Pyramid methods in image processing. RCA Eng. 1984, 29, 33–41. [Google Scholar]
- Förstner, W.; Guelch, E. A Fast operator for detection and precise location of distinct points, corners and centers of circular features. In Proceedings of the ISPRS Intercommission Workshop on Fast Processing of Photogrammetric Data, Interlaken, Switzerland, 2–4 June 1987; pp. 281–305.
- Lowe, D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 2004, 60, 91–110. [Google Scholar] [CrossRef]
- Di, K.; Xu, F.; Wang, J.; Agarwal, S.; Brodyagina, E.; Li, R.; Matthies, L. Photogrammetric processing of rover imagery of the 2003 Mars Exploration Rover mission. ISPRS J. Photogramm. Remote Sens. 2003, 63, 181–201. [Google Scholar] [CrossRef]
- Di, K.; Liu, Z.; Yue, Z. Mars rover localization based on feature matching between ground and orbital imagery. Photogram. Eng. Remote Sens. 2011, 77, 781–791. [Google Scholar] [CrossRef]
- Wu, B.; Guo, J.; Hu, H.; Li, Z.; Chen, Y. Co-registration of lunar topographic models derived from Chang’E-1, SELENE, and LRO laser altimeter data based on a novel surface matching method. Earth Planet. Sci. Lett. 2013, 364, 68–84. [Google Scholar] [CrossRef]
- Pratt, W.K. Digital Image Processing, 2nd ed.; Wiley: New York, NY, USA, 1991. [Google Scholar]
- Lewis, J.P. Fast normalized cross-correlation. Vis. Interface 1995, 10, 120–123. [Google Scholar]
- Förstner, W. On the geometric precision of digital correlation. Int. Arch. Photogramm. Remote Sens. 1982, 24, 176–189. [Google Scholar]
- Gruen, A. Adaptive least squares correlation: A powerful image matching technique. S. Afr. J. Photogramm. Remote Sens. Cartogr. 1985, 14, 175–187. [Google Scholar]
- Gruen, A. Development and status of image matching in photogrammetry. Photogram. Rec. 2012, 27, 36–57. [Google Scholar] [CrossRef]
- Remondino, F.; El-Hakim, S.; Gruen, A.; Zhang, L. Turning images into 3-D models. IEEE Signal. Process. Mag. 2008, 25, 55–65. [Google Scholar] [CrossRef]
- Kruse, F.A.; Lefkoff, A.B.; Boardman, J.W.; Heidebrecht, K.B.; Shapiro, A.T.; Barloon, P.J.; Goetz, A.F.H. The spectral image processing system (SIPS)—Interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 1993, 44, 145–163. [Google Scholar] [CrossRef]
- Keshava, N. Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Trans. Geosci. Remote Sens. 2004, 42, 1552–1565. [Google Scholar] [CrossRef]
- Van der Meer, F. The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery. Int. J. Appl. Earth Obs. Geoinform. 2006, 8, 3–17. [Google Scholar] [CrossRef]
- Yan, L.; Niu, X. Spectral-angle-based Laplacian eigenmaps for nonlinear dimensionality reduction of hyperspectral imagery. Photogramm. Eng. Remote Sens. 2014, 80, 849–861. [Google Scholar] [CrossRef]
- Yan, L.; Roy, D.P. Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction. Remote Sens. Environ. 2015, 158, 478–491. [Google Scholar] [CrossRef]
- Witkin, A.P. Scale-space filtering: A new approach to multi-scale description. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'84), San Diego, CA, USA, 19–21 March 1984; Volume 9, pp. 150–153.
- Toutin, T. Review article: Geometric processing of remote sensing images: Models, algorithms and methods. Int. J. Remote Sens. 2004, 25, 1893–1924. [Google Scholar] [CrossRef]
- Townshend, J.R.; Justice, C.O.; Gurney, C.; McManus, J. The impact of misregistration on change detection. IEEE Trans. Geosci. Remote Sens. 1992, 30, 1054–1060. [Google Scholar] [CrossRef]
- Roy, D.P. The impact of misregistration upon composited wide field of view satellite data and implications for change detection. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2017–2032. [Google Scholar] [CrossRef]
- De Leeuw, A.J.; Veugen, L.M.M.; Van Stokkom, H.T.C. Geometric correction of remotely-sensed imagery using ground control points and orthogonal polynomials. Int. J. Remote Sens. 1988, 9, 1751–1759. [Google Scholar] [CrossRef]
- Roy, D.P.; Zhang, H.K.; Ju, J.; Gomez-Dans, J.L.; Lewis, P.E.; Schaaf, C.B.; Sun, Q.; Li, J.; Huang, H.; Kovalskyy, V. A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance. Remote Sens. Environ. 2016, 176, 255–271. [Google Scholar] [CrossRef]
- Atzberger, C. Remote Sensing Special Issue: First Experiences with European Sentinel-2 Multi-Spectral Imager (MSI). 2016. Available online: https://www.mdpi.com/journal/remotesensing/special_issues/sentinel-2_msi (accessed on 6 June 2016).
Landsat-8 Week 47 Image and Sentinel-2A Week 49 Image Tie-Point Differences (116 Tie-Points) | Landsat-8 Week 47 Image and Sentinel-2A Week 51 Image Tie-Point Differences (797 Tie-Points) | |||
---|---|---|---|---|
minimum | 4.305 | −2.707 | 4.270 | −3.102 |
maximum | 6.321 | −1.484 | 6.326 | −1.354 |
mean | 5.445 | −2.119 | 5.263 | −2.132 |
standard deviation | 0.402 | 0.301 | 0.400 | 0.334 |
Translation Transformation (Equation (1)) | Affine Transformation (Equation (2)) | 2nd Order Polynomial Transformation (Equation (3)) | |||||
---|---|---|---|---|---|---|---|
a0 | −5.479373097 | a0 | −7.167002618 | a0 | −8.803776971 | a3 | −0.000000007 |
a1 | 1.000156515 | a1 | 1.000413024 | a4 | −0.000000011 | ||
a2 | −0.000061958 | a2 | −0.000010265 | a5 | 0.000000004 | ||
b0 | 2.131139260 | b0 | 3.642703772 | b0 | 3.871263951 | b3 | −0.000000000 |
b1 | −0.000105759 | b1 | −0.000129013 | b4 | 0.000000000 | ||
b2 | 0.999971229 | b2 | 0.999933409 | b5 | 0.000000001 | ||
RMSE: 0.504 pixels | RMSE: 0.286 pixels | RMSE: 0.296 pixels |
Translation Transformation (Equation (1)) | Affine Transformation (Equation (2)) | 2nd Order Polynomial Transformation (Equation (3)) | |||||
---|---|---|---|---|---|---|---|
a0 | −5.264385177 | a0 | −7.294159359 | a0 | −8.803776971 | a3 | −0.000000009 |
a1 | 1.000180379 | a1 | 1.000413024 | a4 | −0.000000006 | ||
a2 | −0.000069438 | a2 | −0.000010265 | a5 | 0.000000002 | ||
b0 | 2.130857209 | b0 | 3.678436109 | b0 | 3.871263951 | b3 | 0.000000001 |
b1 | −0.000106223 | b1 | −0.000129013 | b4 | 0.000000002 | ||
b2 | 0.999962709 | b2 | 0.999933409 | b5 | 0.000000001 | ||
RMSE: 0.536 pixels | RMSE: 0.302 pixels | RMSE: 0.301 pixels |
Translation Transformation (Equation (1)) | Affine Transformation (Equation (2)) | 2nd Order Polynomial Transformation (Equation (3)) | |
---|---|---|---|
minimum | −0.215 | 0.000 | 0.002 |
maximum | −0.215 | 0.266 | 1.332 |
mean | −0.215 | 0.114 | 0.245 |
standard deviation | 0 | 0.055 | 0.257 |
Translation Transformation (Equation (1)) | Affine Transformation (Equation (2)) | 2nd Order Polynomial Transformation (Equation (3)) | ||||
---|---|---|---|---|---|---|
47 —> 49 | 47 —> 51 | 47 —> 49 | 47 —> 51 | 47 —> 49 | 47 —> 51 | |
mean | 0.508 | 0.494 | 0.270 | 0.252 | 0.260 | 0.247 |
standard deviation | 0.272 | 0.239 | 0.184 | 0.177 | 0.180 | 0.174 |
minimum | −2.551 | −2.305 |
maximum | −0.512 | −1.035 |
mean | 1.498 | −1.741 |
standard deviation | 0.477 | 0.229 |
Translation Transformation (Equation 1) | Affine Transformation (Equation (2)) | 2nd Order Polynomial Transformation (Equation (3)) | |||||
---|---|---|---|---|---|---|---|
a0 | 1.487925133 | a0 | 0.408828226 | a0 | 0.611637495 | a3 | 0.000000004 |
a1 | 1.000101868 | a1 | 1.000011088 | a4 | 0.000000006 | ||
a2 | 0.000064412 | a2 | 0.000089111 | a5 | −0.000000005 | ||
b0 | 1.743876575 | b0 | 1.934822089 | b0 | 2.094640816 | b3 | 0.000000003 |
b1 | −0.000036935 | b1 | −0.000086720 | b4 | 0.000000002 | ||
b2 | 1.000004653 | b2 | 0.999997931 | b5 | −0.000000001 | ||
RMSE: 0.534 pixels | RMSE: 0.303 pixels | RMSE: 0.309 pixels |
Translation Transformation (Equation (1)) | Affine Transformation (Equation (2)) | 2nd Order Polynomial Transformation (Equation (3)) | |
---|---|---|---|
mean | 0.467 | 0.215 | 0.210 |
standard deviation | 0.274 | 0.173 | 0.170 |
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
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Yan, L.; Roy, D.P.; Zhang, H.; Li, J.; Huang, H. An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery. Remote Sens. 2016, 8, 520. https://doi.org/10.3390/rs8060520
Yan L, Roy DP, Zhang H, Li J, Huang H. An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery. Remote Sensing. 2016; 8(6):520. https://doi.org/10.3390/rs8060520
Chicago/Turabian StyleYan, Lin, David P. Roy, Hankui Zhang, Jian Li, and Haiyan Huang. 2016. "An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery" Remote Sensing 8, no. 6: 520. https://doi.org/10.3390/rs8060520