Advertisement

KSCE Journal of Civil Engineering

, Volume 23, Issue 4, pp 1776–1785 | Cite as

Cost-effective Framework for Rapid Underwater Mapping with Digital Camera and Color Correction Method

  • Anjin Chang
  • Jinha JungEmail author
  • Dugan Um
  • Junho Yeom
  • Frederick Hanselmann
Surveying and Geo-Spatial Engineering
  • 16 Downloads

Abstract

Geo-referenced mapping in an aquatic environment is challenging because it is hard to measure the location of objects and images underwater. In this paper, we propose the method and share the results: cost-effective framework to generate 2D and 3D underwater maps with digital imagery and a Structure from Motion (SfM) algorithm. The proposed method consists of data acquisition, image processing, and color correction. 292 and 437 images were acquired from each study site located in Spring Lake in San Marcos, Texas, U.S.A. Agisoft Photoscan Pro software was used to generate 3D point cloud data and orthomosaic images after feature matching and image alignment from geo-tagged imagery. The mosaic images with high resolution (< 0.2 cm per pixel) were generated with 2D underwater images. After color correction, the red reduction effect was recovered, and the bluer color was removed. The 3D underwater map was generated directly from 3D dense point clouds including geo-coordinates and RGB color information. As a result, the Very High Resolution (VHR) 2D and 3D maps were generated and the topographic surface of underwater structures was obtained in great detail. Although the RMSE were about 1 m, the proposed method provided more detailed surface of underwater features.

Keywords

underwater Structure from Motion (SfM) 3D mapping bathymetry underwater color correction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Atallah, L. and Probert Smith, P. J. (2003). “Using wavelet analysis to classify and segment sonar signals scattered from underwater sea beds.” International Journal of Remote Sensing, RSPSoc, vol. 24, no. 21, pp. 4113–4128. DOI: 10.1080/0143116021000035012.CrossRefGoogle Scholar
  2. Balletti, C., Beltrame, C., Costa, E., Guerra, F., and Vernier, P. (2016) “3D reconstruction of marble shipwreck cargoes based on underwater multi–image photogrammetry.” Digital Applications in Archaeology and Cultural Heritage, vol. 3, no. 1, pp. 1–8. DOI: 10.1016/j.daach.2015.11.003.CrossRefGoogle Scholar
  3. Balletti, C., Guerra, F., Tsioukas, V., and Varnier, P. (2014). “Calibration of action cameras for photogrammetric purposes.” Sensors, MDPI, Vol. 14, vol. 9, pp. 17471–17490. DOI: 10.3390/s140917471.CrossRefGoogle Scholar
  4. Bianco, G., Muzzupappa, M., Bruno, F., Garcia, R., and Neumann, L. (2015). “A new color correction method for underwater imaging.” Proc. ISPRS XL–5/W5, ISPRS, Piano di Sorrento, Italy, pp. 25–32. DOI: 10.5194/isprsarchives–XL–5–W5–25–2015.Google Scholar
  5. Brown, C. J. and Blondel, P. (2009). “Developments in the application of multibeam sonar backscatter for seafloor habitat mapping.” Applied Acoustics, Elsevier, vol. 70, no. 10, pp. 1242–1247. DOI: 10.1016/j.apacoust.2008.08.004.CrossRefGoogle Scholar
  6. Dietrich, J. T. (2017). “Bathymetric structure–from–motion: Extracting shallow stream bathymetry from multi–view stereo photogrammetry.” Earth Surface Processes and Landforms, Wiley, vol. 42, no. 2, pp. 355–364. DOI: 10.1002/esp.4060.CrossRefGoogle Scholar
  7. Ferrari, R., McKinnon, D., He, H., Smith, R. N., Corke, P., González–Rivero, M., Mumby, P. J., and Upcroft, B. (2016). “Quantifying multiscale habitat structural complexity: A cost–effective framework for underwater 3D modelling.” Remote Sensing, MDPI, vol. 8, no. 2, pp. 113, DOI: 10.3390/rs8020113.CrossRefGoogle Scholar
  8. Figueira, W., Renata, F., Weatherby, E., Porter, A., Hawes, S. and Byrne, M. (2015). “Accuracy and precision of habitat structural complexity metrics derived from underwater photogrammetry.” Remote Sensing, MDPI, vol. 7, no. 12, pp. 16883–16900. DOI: 10.3390/rs71215859.CrossRefGoogle Scholar
  9. Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., and Carbonneau, P. E. (2017). “Topographic structure from motion: A new development in photogrammetric measurement.” Earth Surface Processes and Landforms, Wiley, vol. 38, no. 4, pp. 421–430. DOI: 10.1002/esp.3366.CrossRefGoogle Scholar
  10. González–Rivero, M., Beijbom, O., Rodriguez–Ramirez, A., Holtrop, T., González–Marrero, Y., Ganase, A., Roelfsema, C., Phinn, S., and Hoegh–Guldberg, O. (2016). “Scaling up ecological measurements of coral reefs using semi–automated field image collection and analysis.” Remote Sensing, MDPI, Vol. 8, No. 1, p. 30, DOI: 10.3390/rs8010030. Google Street View (n.d.). https://www.google.com/streetview/#oceans [Accessed on Sept. 21, 2018].Google Scholar
  11. Harwin, S., Lucieer, A., and Osborn, J. (2015). “The impact of the calibration method on the accuracy of point clouds derived using unmanned aerial vehicle multi–view stereopsis.” Remote Sensing, MDPI, vol. 7, no. 9, pp. 11933–11953. DOI: 10.3390/rs70911933.CrossRefGoogle Scholar
  12. Hasan, R. C., Ierodiaconou, D., and Monk, J. (2012). “Evaluation of four supervised learning methods for benthic habitat mapping using backscatter from multi–beam sonar.” Remote Sensing, MDPI, vol. 4, no. 11, pp. 3427–3443. DOI: 10.3390/rs4113427.CrossRefGoogle Scholar
  13. Hooge, J., Hanselmann, F. H., Lohse, J. C., and Warren, D. (2016). Spring lake underwater geoarchaeology survey, The Meadow Center for Water and the Environment Center for Archaeological Studies, Texas State University, San Marcos, TX, USA.Google Scholar
  14. Johnson–Roberson, M., Pizarro, O., Williams, S. B., and Mahon, I. (2010). “Generation and visualization of large–scale three–dimensional reconstructions from underwater robotic surveys.” Journal of Field Robot, Wiley, vol. 27, no. 1, pp. 21–51. DOI: 10.1002/rob.20324.CrossRefGoogle Scholar
  15. Lowe, D. G. (2014). “Distinctive image features from scale–invariant keypoints.” International Journal of Computer Vision, Springer, vol. 60, no. 2, pp. 91–110. DOI: 10.1023/B:VISI.0000029664.99615.94.CrossRefGoogle Scholar
  16. Lu, D. and Cho, H. J. (2010). “An improved water–depth correction algorithm for seagrass mapping using hyperspectral data.” Remote Sensing Letters, RSPSoc, vol. 2, no. 2, pp. 91–97. DOI: 10.1080/01431161.2010.502152.CrossRefGoogle Scholar
  17. Reinhard, E., Ashikhmin, M., Gooch, B., and Shirley, P. (2001). “Color transfer between images.” IEEE Computer Graphics and Applications, IEEE, vol. 21, no. 5, pp. 34–41. DOI: 10.1109/38.946629.CrossRefGoogle Scholar
  18. Roberts, J. M., Brown, C. J., Long, D., and Bates, C. R. (2005). “Acoustic mapping using a multibeam echosounder reveals cold–water coral reefs and surrounding habitats.” Coral Reefs, Springer, vol. 24, no. 4, pp. 654–669. DOI: 10.1007/s00338–005–0049–6.CrossRefGoogle Scholar
  19. Roman, C. N., Inglis, G., Vaughn, J. I., Williams, S., Pizarro, O., Friedman, A., and Steinberg, D. (2011). “Development of highresolution underwater mapping techniques,” Oceanography, The Oceanography Society, Vol. 24, No. 1, supplement, pp. 42–45. DOI: 10.5670/oceanog.24.1.supplement.Google Scholar
  20. Ruderman, D., Cronin, T., and Chiao, C. (1998). “Statistics of cone responses to natural images: Implications for visual coding.” Journal of the Optical Society of America A, OSA, vol. 15, no. 8, pp. 2036–2045. DOI: 10.1364/JOSAA.15.002036.CrossRefGoogle Scholar
  21. Schobesberger, D. and Patterson, T. (2008). “Evaluating the effectiveness of 2D vs. 3D trailhead maps.” Proc. 6th ICA Mountain Cartography Workshop, ICA, Lenk, Switzerland, pp. 201–205.Google Scholar
  22. Schuetz, M. (2016). Potree: Rendering large point clouds in web browsers, PhD Thesis, Vienna University of Technology, Wien, Austria.Google Scholar
  23. Shao, Z., Yang, N., Xiao, X., Zhang, L., and Peng, Z. (2016). “A multiview dense point cloud generation algorithm based on low–altitude remote sensing images.” Remote Sensing, MDPI, Vol. 8, Vol. 5, pp. 381, DOI: 10.3390/rs8050381.Google Scholar
  24. Snavely, N., Seitz, S. M., and Szeliski, R. (2008). “Modeling the world from internet photo collections.” International Journal of Computer Vision, Springer, vol. 80, no. 2, pp. 189–210. DOI: 10.1007/s11263–007–0107–3.CrossRefGoogle Scholar
  25. USGS (n.d.). “The national map: 3D elevation program.” US Geological Survey, https://www.usgs.gov/core–science–systems/ngp/3dep [Accessed on Sept. 21, 2018].Google Scholar
  26. VanMiddlesworth, M., Kaess, M., Hover, F. S., and Leonard, J. J. (2015). “Mapping 3D underwater environments with smoothed submaps.” Field and Service Robotics, L. Mejias, P. Corke, and J. Roberts, Ed., Springer, Cham, pp. 17–30. DOI: 10.1007/978–3–319–07488–7_2.Google Scholar
  27. Vierling, K. T., Vierling, L. A., Gould, W. A., Martinuzzi, S., and Clawges, R. M. (2008) “Lidar: Shedding new light on habitat characterization and modeling.” Frontiers in Ecology and the Environment, ESA, vol. 6, no. 2, pp. 90–98. DOI: 10.1890/070001.CrossRefGoogle Scholar
  28. Walker, B. K., Riegl, B., and Dodge, R. E. (2008). “Mapping coral reef habitats in Southeast Florida using a combined technique approach.” Journal of Coastal Research, CERF, vol. 24, no. 5, pp. 1138–1150. DOI: 10.2112/06–0809.1.CrossRefGoogle Scholar
  29. Wang, C. K. and Philpot, W. D. (2007). “Using airborne bathymetric lidar to detect bottom type variation in shallow waters.” Remote Sensing of Environment, Elsevier, vol. 106, no. 1, pp. 123–135. DOI: 10.1016/j.rse.2006.08.003.CrossRefGoogle Scholar
  30. Wolf, P. R. and DeWitte, B. A. (2000). Elements of photogrammetry: With applications in GIS, McGraw–Hill, New York, NY, USA.Google Scholar
  31. Zavalas, R., Ierodiaconou, D., Ryan, D., Rattray, A., and Monk, J. (2014). “Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR,” Remote Sensing, MDPI, vol. 6, no. 3, pp. 2154–5175. DOI: 10.3390/rs6032154.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers 2019

Authors and Affiliations

  • Anjin Chang
    • 1
  • Jinha Jung
    • 1
    Email author
  • Dugan Um
    • 1
  • Junho Yeom
    • 2
  • Frederick Hanselmann
    • 3
  1. 1.School of Engineering and Computing SciencesTexas A&M University-Corpus ChristiCorpus ChristiUSA
  2. 2.Research Institute for Automotive Diagnosis Technology of Multi-scale Organic and Inorganic StructureKyungpook National UniversitySangjuKorea
  3. 3.Underwater Archaeology and Underwater Exploration, Dept. of Marine Ecosystems and SocietyUniversity of MiamiMiamiUSA

Personalised recommendations