Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Remote Sensingarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Remote Sensing
Other literature type . Article . 2019 . Peer-reviewed
License: CC BY
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Ktisis
Article . 2019
Data sources: Ktisis
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Remote Sensing
Article . 2019
Data sources: DOAJ-Articles
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Remote Sensing
Article
License: CC BY
Data sources: UnpayWall
versions View all 4 versions

DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds

Authors: Panagiotis Agrafiotis; Dimitrios Skarlatos; Andreas Georgopoulos; Konstantinos Karantzalos;

DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds

Abstract

The determination of accurate bathymetric information is a key element for near offshore activities; hydrological studies, such as coastal engineering applications, sedimentary processes, hydrographic surveying, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide a low-cost alternative compared to bathymetric LiDAR (Light Detection and Ranging) surveys, as it offers additional important visual information and higher spatial resolution. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this article, in order to overcome the water refraction errors in a massive and accurate way, we employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths. In particular, an SVR (support vector regression) model was developed, based on known depth observations from bathymetric LiDAR surveys, which is able to accurately recover bathymetry from point clouds derived from SfM-MVS procedures. Experimental results and validation were based on datasets derived from different test-sites, and demonstrated the high potential of our approach. Moreover, we exploited the fusion of LiDAR and image-based point clouds towards addressing challenges of both modalities in problematic areas.

Country
Cyprus
Related Organizations
Subjects by Vocabulary

Microsoft Academic Graph classification: Point cloud Hydrographic survey Structure from motion Bathymetry Image resolution Remote sensing Refraction Support vector machine Lidar Geology

Keywords

fusion, LiDAR, Science, SVM, UAV, bathymetry, Civil Engineering, seabed mapping, aerial imagery, Machine learning, Refraction effect, Fusion, data integration, Q, Seabed mapping, Point cloud, point cloud; bathymetry; SVM; machine learning; UAV; aerial imagery; seabed mapping; refraction effect; LiDAR; fusion; data integration, machine learning, Bathymetry, refraction effect, Engineering and Technology, General Earth and Planetary Sciences, Data integration, Aerial imagery, point cloud

76 references, page 1 of 8

1. Agrafiotis, P.; Skarlatos, D.; Forbes, T.; Poullis, C.; Skamantzari, M.; Georgopoulos, A. Underwater photogrammetry in very shallow waters: main challenges and caustics e ect removal. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2018, XLII-2, 15-22. [CrossRef]

2. Karara, H.M. Non-Topographic Photogrammetry, 2nd ed.; American Society for Photogrammetry and Remote Sensing: Falls Church, VA, USA, 1989.

3. Menna, F.; Agrafiotis, P.; Georgopoulos, A. State of the art and applications in archaeological underwater 3D recording and mapping. J. Cult. Herit. 2018. [CrossRef]

4. 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. [CrossRef]

5. Green, E.; Mumby, P.; Edwards, A.; Clark, C. Remote Sensing: Handbook for Tropical Coastal Management; United Nations Educational Scientific and Cultural Organization (UNESCO): London, UK, 2000.

6. Agrafiotis, P.; Skarlatos, D.; Georgopoulos, A.; Karantzalos, K. Shallow water bathymetry mapping from UAV imagery based on machine learning. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2019, XLII-2/W10, 9-16. [CrossRef]

7. Lavest, J.; Rives, G.; Lapresté, J. Underwater camera calibration. In Computer Vision-ECCV; Vernon, D., Ed.; Springer: Berlin, Germany, 2000; pp. 654-668.

8. Shortis, M. Camera Calibration Techniques for Accurate Measurement Underwater. In 3D Recording and Interpretation for Maritime Archaeology; Springer: Cham, Switzerland, 2019; pp. 11-27.

9. Elnashef, B.; Filin, S. Direct linear and refraction-invariant pose estimation and calibration model for underwater imaging. ISPRS J. Photogramm. Remote Sens. 2019, 154, 259-271. [CrossRef]

10. Fryer, J.G.; Kniest, H.T. Errors in Depth Determination Caused by Waves in Through-Water Photogrammetry. Photogramm. Rec. 1985, 11, 745-753. [CrossRef]

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    28
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    28
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
    Powered byBIP!BIP!
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Top 10%
Average
Top 10%
gold
moresidebar

Do the share buttons not appear? Please make sure, any blocking addon is disabled, and then reload the page.