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

Panagiotis Agrafiotis*     Dimitrios Skarlatos    Andreas Georgopoulos     Konstantinos Karantzalos    

Department of Topography, School of Rural and Surveying Engineering, Zografou Campus, National Technical University of Athens, 9 Heroon Polytechniou str., 15780 Athens, Greece

Lab of Photogrammetric Vision, Civil Engineering and Geomatics Department, Cyprus University of Technology, 2-8 Saripolou str., 3036 Limassol, Cyprus

Remote Sensing 2019, 11(19), 2225

[paper] [demo (TBA)]

Figure 1. DepthLearn's concept We employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths in order to deliver accurate bathymetric information


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.

Refraction correction results

Figure 2. Mean distances. The histograms of the M3C2 distances between the LiDAR point cloud and the corrected point clouds after the application of the proposed approach in relation to the real depth. The red dashed lines represent the accuracy limits generally accepted for hydrography, as introduced by the International Hydrographic Organization (IHO).

Seabed cross-sections

Figure 3. Cross-sections. Indicative parts of the cross-sections (X and Y axes have the same scale) from the Agia Napa (Part I) region after the application of the proposed approach when trained with 30% from the Part II region (first column); the Amathouda test site was predicted with the model trained on Dekelia (middle column); and the Dekelia test site with the model trained on Merged dataset (right column).

Point Cloud fusion

Figure 4. Fusion results. Three different sample areas of the Agia Napa seabed with depths between 9.95 m to 11.75 m. The first two columns depict two sample areas of the Agia Napa (I) seabed, while the third depicts a sample area of the Agia Napa (II) seabed. The first row depicts the three areas on the image-based point cloud; the second row, image on the LiDAR point cloud colored with the corrected image-based point cloud; and the third row depicts the three areas on the LiDAR point cloud, colored using the uncorrected image-based one. Point size is multiplied by a factor of four in order to facilitate visual comparisons.