# A Novel Iterative Water Refraction Correction Algorithm for Use in Structure from Motion Photogrammetric Pipeline

^{*}

## Abstract

**:**

## 1. Introduction

#### Previous Work

## 2. Methodology

#### 2.1. Discrepancy Estimation

#### 2.1.1. Shan’s Model

**α**and

**β**respectively [16].

**s**value at each collinearity equation to correct explicitly the depth of each point. This s can be calculated iteratively, since it is an expression of the water refraction index n and the refraction angle α.

**Z**correction index (caused by the refraction). As shown in Figure 1 C (X, Y, Z’), S (Xs, Ys, Zs) and point a on the image plane are collinear, so that it is possible to write directly that:

**s**for each collinearity equation. Therefore, the photogrammetric intersection becomes a system with 4 observations and 5 unknowns (X, Y, Z, s

_{left}, s

_{right}), which can only be solved iteratively.

**s**is calculated through Equation (7) and reinserted in Equation (6) for the second approximation. Iterations stop when the difference among the two consecutive values for tan (a), is within tolerance.

#### 2.1.2. Examination of Water Refraction Effect in Drone Photography

_{1}and a

_{2}, can be backtracked in photos O

_{1}and O

_{2}, using the standard collinearity equation—the one used for forward calculation of the point’s coordinates, without refraction taken into consideration. If a point has been matched successfully in two photos O

_{1}and O

_{2}, then the standard collinearity intersection would have returned the point C, which is the apparent position of point A.

_{1}C and A

_{2}C will not intersect except in the special case where the point A is equidistant from the camera stations. If the systematic error resulting from the refraction effect is ignored, then the two lines O

_{1}A

_{1}and O

_{2}A

_{2}do not intersect exactly on the normal, passing from the underwater point A, but approximately at C, the apparent depth of the point. Thus, without some form of correction, refraction acts to produce an image of the surface which appears to lie at a shallower depth than the real surface, and it is worthy of attention that in each shot the collinearity condition is violated [10].

_{1}and a

_{2}are homologous points in the photo plane, and they represent the same point in object space, the correct point can be calculated explicitly (deterministic). Given that the sea level (refraction surface) is the XY plane of the reference system, all points in it (such as A

_{1}and A

_{2}) have Z = 0 and their 3D space coordinates can be directly calculated using collinearity (Equation (8), which is linearly solved with only three unknowns. Having calculated the points A

_{1}and A

_{2}on the refraction surface:

- The water surface is planar, without waves;
- The water surface level is the reference (Z = 0) of the coordinate system;
- The photo coordinates have been already corrected with respect to principal center and lens distortion.

#### 2.1.3. Snell’s Law in Vector Form

_{1}and n

_{2}are the refraction indices, and a

_{1}and a

_{2}the incidence and refraction angles respectively.

#### 2.1.4. Vector Intersection

_{1}is already calculated in a previous step, hence point A in the sea bottom is described by the equation $\overrightarrow{\mathrm{A}}=\overrightarrow{{\mathrm{A}}_{1}}{+\mathrm{t}}_{1}\overrightarrow{{\mathrm{v}}_{2}}$, with t

_{1}unknown. A similar equation can be formed from the second collinearity at point A

_{2}, $\overrightarrow{\mathrm{A}}=\overrightarrow{{\mathrm{A}}_{2}}{+\mathrm{t}}_{2}\overrightarrow{{\mathrm{v}}_{2}^{\prime}}$, with $\overrightarrow{{\mathrm{v}}_{2}^{\prime}}$ having been calculated from vector $\overrightarrow{{\mathrm{O}}_{2}{\mathrm{A}}_{2}}$ transformed by Snell’s law. These two equations, in Cartesian format, can be rewritten as follow

_{A}, Y

_{A}, Z

_{A}, t

_{1}, t

_{2}) with one degree of freedom, similar to the intersection of two collinearity equations, and can be solved linearly using least squares. This two-step solution of the collinearity equation intersection with water refraction is deterministic (direct, no iterations), and can easily be modified to address multi ray intersection instead of a stereopair.

#### 2.1.5. Validation and Error Evaluation

#### 2.2. Proposed Correction Model

_{A}, Y

_{A}, Z

_{A}) using interior and exterior orientation of the photo, and an initial (provisional) (DSM). The line-surface intersection using a single collinearity function, is an iterative process itself, but the Z component can be easily calculated. The initial (provisional) DSM can be created by any commercial photogrammetric software (such as Agisoft’s Photoscan, used in this study) following standard process, without water refraction compensation. An inherent advantage of the proposed method is that using georeferenced photos following bundle adjustment, it is easy to identify if a point lies on the ground or sea bed by its height in the DSM. If point A has Z

_{A}, above zero (usually zero is assigned to mean sea level), it can be omitted from further processing. If zero does not correspond to sea level then a reference point on the seashore, gathered during control point acquisition, may indicate the water surface. Additionally, lakes can also be accommodated by a reference point in the shore and setting the proper water level Z in the code.

_{air}and P

_{water}are the percentages of the ray travelling on air and water respectively, and n

_{air}and n

_{water}the refraction indexes respectively, it is easy to calculate c

_{mixed}by using camera focal length on air (c

_{air}), considering n

_{air}as 1 and approximating n

_{water}by Equation (13) [33] or measuring it directly.

_{A}) of point A (Figure 7). It is obvious that the triangles in Figure 7 are not similar, but for approximate (iteration, provisional) values this assumption is sufficient.

_{mixed}for the point A, a new position of a, at photo plane, can be calculated. Having estimated c

_{mixed}for this particular point, a new position (a’ on Figure 8), of it may be calculated for the c

_{air}. Since c

_{mixed}will be larger in all depth cases (n

_{water}> n

_{air}), this will cause an overall photo shrinkage, hence raw information will be kept within the initial photo frame, while areas with missing information due to shrinkage will be kept black.

#### 2.3. Implementation Aspects of the Proposed Methodology

#### 2.4. Other Assumptions

#### 2.4.1. Bundle Adjustment

#### 2.4.2. Wave Effect

## 3. Application and Verification on Test Sites

#### 3.1. Amathounta Test Site

#### 3.2. Agia Napa Test Site

#### 3.3. Flight Planning

#### 3.4. LiDAR Reference Data

#### 3.5. Evaluation with Reference Data

#### 3.5.1. Cloud-to-Cloud Distances

#### 3.5.2. Seabed Cross Sections

## 4. Concluding Remarks

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Karara, H.M. Non-Topographic Photogrammetry, 2nd ed.; American Society for Photogrammetry and Remote Sensing: Falls Church, VA, USA, 1989. [Google Scholar]
- Menna, F.; Agrafiotis, P.; Georgopoulos, A. State of the art and applications in archaeological underwater 3D recording and mapping. J. Cult. Herit.
**2018**. [Google Scholar] [CrossRef] - Skarlatos, D.; Savvidou, E. Coastal Survey of archaeological sites using drones. Skyllis
**2015**, 15, 196–204. [Google Scholar] - Georgopoulos, A.; Agrafiotis, P. Documentation of a submerged monument using improved two media techniques. In Proceedings of the 2012 18th International Conference on Virtual Systems and Multimedia, Milan, Italy, 2–5 September 2012; pp. 173–180. [Google Scholar] [CrossRef]
- Butler, J.B.; Lane, S.N.; Chandler, J.H.; Porfiri, E. Through-water close range digital photogrammetry in flume and field environments. Photogramm. Rec.
**2002**, 17, 419–439. [Google Scholar] [CrossRef] - Sedlazeck, A.; Koch, R. Perspective and non-perspective camera models in underwater imaging—Overview and error analysis. In Outdoor and Large-Scale Real-World Scene Analysis; Springer: Berlin/Heidelberg, Germany, 2012; pp. 212–242. [Google Scholar]
- Constantinou, C.C.; Loizou, S.G.; Georgiades, G.P.; Potyagaylo, S.; Skarlatos, D. Adaptive calibration of an underwater robot vision system based on hemispherical optics. In Proceedings of the IEEE/OES Autonomous Underwater Vehicles (AUV), Oxford, MS, USA, 6–9 October 2014. [Google Scholar]
- Fryer, J.G.; Fraser, C.S. On the calibration of underwater cameras. Photogramm. Rec.
**1986**, 12, 73–85. [Google Scholar] [CrossRef] - Lavest, J.; Rives, G.; Lapresté, J. Underwater camera calibration. In Computer Vision—ECCV 2000; Vernon, D., Ed.; Springer: Berlin, Germany, 2000; pp. 654–668. [Google Scholar]
- Fryer, J.G.; Kniest, H.T. Errors in Depth Determination Caused by Waves in Through-Water Photogrammetry. Photogramm. Rec.
**1985**, 11, 745–753. [Google Scholar] [CrossRef] - Okamoto, A. Wave influences in two-media photogrammetry. Photogramm. Eng. Remote Sens.
**1982**, 48, 1487–1499. [Google Scholar] - Agrafiotis, P.; Georgopoulos, A. Camera constant in the case of two media photogrammetry. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2015**, XL-5/W5, 1–6. [Google Scholar] [CrossRef] - Tewinkel, G.C. Water depths from aerial photographs. Photogramm. Eng.
**1963**, 29, 1037–1042. [Google Scholar] - Shmutter, B.; Bonfiglioli, L. Orientation problem in two-medium photogrammetry. Photogramm. Eng.
**1967**, 33, 1421–1428. [Google Scholar] - Wang, Z. Principles of Photogrammetry (with Remote Sensing); Publishing House of Surveying and Mapping: Beijing, China, 1990. [Google Scholar]
- Shan, J. Relative orientation for two-media photogrammetry. Photogramm. Rec.
**1994**, 14, 993–999. [Google Scholar] [CrossRef] - Fryer, J.F. Photogrammetry through shallow waters. Aust. J. Geod. Photogramm. Surv.
**1983**, 38, 25–38. [Google Scholar] - Maas, H.-G. On the Accuracy Potential in Underwater/Multimedia Photogrammetry. Sensors
**2015**, 15, 18140–18152. [Google Scholar] [CrossRef] [PubMed][Green Version] - Whittlesey, J.H. Elevated and airborne photogrammetry and stereo photography. Photogr. Archaeol. Res.
**1975**, 223–259. [Google Scholar] - Westaway, R.; Lane, S.; Hicks, M. Remote sensing of clear-water, shallow, gravel-bed rivers using digital photogrammetry. Photogramm. Eng. Remote Sens.
**2001**, 67, 1271–1281. [Google Scholar] - Elfick, M.H.; Fryer, J.G. Mapping in shallow water. Int. Arch. Photogramm. Remote Sens.
**1984**, 25, 240–247. [Google Scholar] - Ferreira, R.; Costeira, J.P.; Silvestre, C.; Sousa, I.; Santos, J.A. Using stereo image reconstruction to survey scale models of rubble-mound structures. In Proceedings of the First International Conference on the Application of Physical Modelling to Port and Coastal Protection, Porto, Portugal, 8–10 May 2006. [Google Scholar]
- Muslow, C. A flexible multi-media bundle approach. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2010**, 38, 472–477. [Google Scholar] - Wolff, K.; Forstner, W. Exploiting the multi view geometry for automatic surfaces reconstruction using feature based matching in multi media photogrammetry. Int. Arch. Photogramm. Remote Sens.
**2000**, 33, 900–907. [Google Scholar] - Ke, X.; Sutton, M.A.; Lessner, S.M.; Yost, M. Robust stereo vision and calibration methodology for accurate three-dimensional digital image correlation measurements on submerged objects. J. Strain Anal. Eng. Des.
**2008**, 43, 689–704. [Google Scholar] [CrossRef] - Byrne, P.M.; Honey, F.R. Air survey and satellite imagery tools for shallow water bathymetry. In Proceedings of the 20th Australian Survey Congress, Shrewsbury, UK, 20 May 1977; pp. 103–119. [Google Scholar]
- Harris, W.D.; Umbach, M.J. Underwater mapping. Photogramm. Eng.
**1972**, 38, 765. [Google Scholar] - Masry, S.E. Measurement of water depth by the analytical plotter. Int. Hydrogr. Rev.
**1975**, 52, 75–86. [Google Scholar] - Dietrich, J.T. Bathymetric Structure-from-Motion: Extracting shallow stream bathymetry from multi-view stereo photogrammetry. Earth Surf. Process. Landf.
**2017**, 42, 355–364. [Google Scholar] [CrossRef] - Telem, G.; Filin, S. Photogrammetric modeling of underwater environments. ISPRS J. Photogramm. Remote Sens.
**2010**, 65, 433–444. [Google Scholar] [CrossRef] - Jordan, E.K. Handbuch der Vermessungskunde; J.B. Metzlersche Verlagsbuchhandlung: Stuttgart, Germany, 1972. [Google Scholar]
- StarkEffects.com. Snell’s Law in Vector Form. 2018. Available online: http://www.starkeffects.com/snells-law-vector.shtml (accessed on 26 June 2018).
- Quan, X.; Fry, E. Empirical equation for the index of refraction of seawater. Appl. Opt.
**1995**, 34, 3477–3480. [Google Scholar] [CrossRef] [PubMed] - Masry, S.E.; MacRitchie, S. Different considerations in coastal mapping. Photogramm. Eng. Remote Sens.
**1980**, 46, 521–528. [Google Scholar] - Mandlburger, G.; Otepka, J.; Karel, W.; Wagner, W.; Pfeifer, N. Orientation and Processing of Airborne Laser Scanning data (OPALS)—Concept and first results of a comprehensive ALS software. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Proceedings of the ISPRS Workshop Laserscanning ’09, Paris, France, 1–2 September 2009; Bretar, F., Pierrot-Deseilligny, M., Vosselman, G., Eds.; Société Française de Photogrammétrie et de Télédétection: Marne-la-vallée, France, 2009. [Google Scholar]
- Steinbacher, F.; Pfennigbauer, M.; Aufleger, M.; Ullrich, A. High resolution airborne shallow water mapping. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Proceedings of the XXII ISPRS Congress, Melbourne, Australia 25 August–1 September 2012; 2012; Volume 39, p. B1. [Google Scholar]
- CloudCompare (Version 2.10.alpha) [GPL Software]. 2018. Available online: http://www.cloudcompare.org/ (accessed on 26 June 2018).

**Figure 1.**Geometry of single photo formation [16].

**Figure 2.**The geometry of two-media photogrammetry in the stereo case [10].

**Figure 4.**Real (

**blue**) and apparent (

**green**) sea bottom, with their difference (

**red**), based on the Sensfly SwingletCAM scenario, of 100 m flying height and 80% overlap.

**Figure 5.**Difference of apparent and correct depth, for depths 0–15 m, at 1 m step, if acquired with a SwingletCAM from 100 m (

**left**) and 200 m (

**right**), flying height, with 80% overlap.

**Figure 6.**Difference of apparent and correct depth, for depths 0–15 m, if acquired with an Vexcel UltraCamX with 100.5 mm lense, with 60% overlap (

**top row**), 80% overlap (

**bottom row**), from 1000 m height (

**left column**), and 3000 m, (

**right column**).

**Figure 11.**The two test sites. Amathounta (

**a**) with lidar data over a 5 m grid (overlaid) and Ag. Napa (

**b**) with original lidar data. On the latter, the data are not overlaid due to high density, which would completely cover the photo.

**Figure 12.**Amathounta test site: camera locations and photo overlap (

**left**) and reconstructed digital elevation model (

**right**).

**Figure 13.**Agia Napa test site: camera locations and photo overlap (

**left**) and reconstructed digital elevation model (

**right**).

**Figure 14.**Amathounta test site (

**left**) and Agia Napa test site (

**right**). The selected areas for applying the tests are illustrated with red-green-blue (RGB) colours while the rest area in greyscale.

**Figure 15.**The cloud-to-cloud distances between the initial point cloud and the point clouds that resulted at each iteration of the algorithm on the Amathouda test site. Row (

**a**) presents the cloud-to-cloud distances between the original point cloud and the point cloud that resulted from the first iteration of the algorithm and their respective histogram. Rows (

**b**–

**d**) present the cloud-to-cloud distances between the initial point cloud and the point cloud that resulted from the second, third and fourth iteration of the algorithm, respectively.

**Figure 16.**The cloud-to-cloud distances between the LiDAR point cloud and the point clouds that resulted from each iteration of the algorithm on the Amathouda test site. Row (

**a**) presents the cloud-to-cloud distances between the LiDAR point cloud and the initial point cloud from the uncorrected photos while rows (

**b**–

**e**) present the cloud-to-cloud distances between the LiDAR point cloud and the point cloud that resulted from the first, second, third and fourth iteration of the algorithm respectively. Residuals of 3rd and 4th iterations are similar (below the ground pixel size), hence there is no significant improvement after the 3rd iteration.

**Figure 17.**The cloud-to-cloud distances between the initial point cloud and the point clouds that resulted from each iteration of the algorithm on the Agia Napa test site. Row (

**a**) presents the cloud-to-cloud distances between the initial point cloud and the point cloud that resulted from the first iteration of the algorithm and their respective histogram. Rows (

**b**–

**d**) present the cloud-to-cloud distances between the initial point cloud and the point cloud that resulted from the second, third and fourth iteration of the algorithm respectively.

**Figure 18.**The cloud-to-cloud distances between the LiDAR point cloud and the point clouds that resulted from each iteration of the algorithm on the Agia Napa test site. Row (

**a**) presents the cloud-to-cloud distances between the LiDAR point cloud and the initial point cloud from the uncorrected photos while rows (

**b**–

**e**) present the cloud-to-cloud distances between the LiDAR point cloud and the point cloud that resulted from the first, second, third and fourth iteration of the algorithm respectively. Residuals of 3rd and 4th iterations are similar (below the ground pixel size), hence there is no significant improvement after the 3rd iteration.

**Figure 20.**Parts of the cross sections having the same scale on X and Y axis. The blue line on the top of each cross section is the water surface while the bold green line is the section on the LiDAR data. (

**a**) is the middle part of Section 1; (

**b**) is the northern part of Section 2; (

**c**) is the northern part of Section 3 (

**d**) the middle part of Section 4; (

**e**) is the middle part of Section 5 and (

**f**) is the deepest part (south-west) of Section 4.

Test Site | Photos | Average Height (m) | GSD (m) | Control Points | SfM-MVS | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|

RMS_{X} (m) | RMS_{Y} (m) | RMS_{Z} (m) | Reprojection Error on All Points (Pixel) | Reprojection Error in Control Points (Pixel) | Total Number of Tie Points | Dense Points | Coverage Area (sq. Km) | ||||

Amathounta | 182 | 103 | 0.033 | 0.0277 | 0.0333 | 0.0457 | 0.645 | 1.48 | 28.5 K | 17.3 M | 0.37 |

Agia Napa | 383 | 209 | 0.063 | 5.03 | 4.74 | 7.36 | 1.106 | 0.76 | 404 K | 8.5 M | 2.43 |

Test Site | Number of LiDAR Points Used | LiDAR Points Density (Points/m ^{2}) | Average Pulse Spacing (m) | LiDAR Flight Height (m) | Accuracy (m) |
---|---|---|---|---|---|

Amathouda | 6.030 | 0.4 | - | 960 | 0.1 |

Agia Napa | 1.288.760 | 1.1 | 1.65 | 960 | 0.1 |

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**MDPI and ACS Style**

Skarlatos, D.; Agrafiotis, P. A Novel Iterative Water Refraction Correction Algorithm for Use in Structure from Motion Photogrammetric Pipeline. *J. Mar. Sci. Eng.* **2018**, *6*, 77.
https://doi.org/10.3390/jmse6030077

**AMA Style**

Skarlatos D, Agrafiotis P. A Novel Iterative Water Refraction Correction Algorithm for Use in Structure from Motion Photogrammetric Pipeline. *Journal of Marine Science and Engineering*. 2018; 6(3):77.
https://doi.org/10.3390/jmse6030077

**Chicago/Turabian Style**

Skarlatos, Dimitrios, and Panagiotis Agrafiotis. 2018. "A Novel Iterative Water Refraction Correction Algorithm for Use in Structure from Motion Photogrammetric Pipeline" *Journal of Marine Science and Engineering* 6, no. 3: 77.
https://doi.org/10.3390/jmse6030077