Shallow Water Bathymetry Mapping from UAV Imagery based on Machine Learning

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 as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. 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 paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach.Comment: 8 pages, 9 figure

FOVEON VS BAYER: COMPARISON OF 3D RECONSTRUCTION PERFORMANCES

The main idea of this particular study was to validate if the new FOVEON technology implemented by sigma cameras can provide better overall results and outperform the traditional Bayer pattern sensor cameras regarding the radiometric information that records as well as the photogrammetric point cloud quality that can provide. Based on that, the scope of this paper is separated into two evaluations. First task is to evaluate the quality of information reconstructed during de-mosaicking step for Bayer pattern cameras by detecting potential additional colour distortion added during the de-mosaicking step, and second task is the geometric comparisons of point clouds generated by the photos by Bayer and FOVEON sensors against a reference point cloud. The first phase of the study is done using various de-mosaicking algorithms to process various artificial Bayern pattern images and then compare them with reference FOVEON images. The second phase of the study is carried on by reconstructing 3D point clouds of the same objects captured by a Bayer and a FOVEON sensor respectively and then comparing the various point clouds with a reference one, generated by a structured light hand-held scanner. The comparison is separated into two parts, where initially we evaluate five separate point clouds (RGB, Gray, Red, Green, Blue) for each camera sensor per site and then a second comparison is evaluated on colour classified RGB point cloud segments

SHALLOW WATER BATHYMETRY MAPPING FROM UAV IMAGERY BASED ON MACHINE LEARNING

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 as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. 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 paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach

GEOMATICS AND CIVIL ENGINEERING INNOVATIVE RESEARCH ON HERITAGE: INTRODUCING THE “ENGINEER” PROJECT

This paper aims to introduce the concept and objectives of a recently supported European project entitled “Geomatics and Civil Engineering Innovative Research on Heritage”, in short ENGINEER. The ENGINEER project visions to enhance and extend inter-departmental multidisciplinary research activities of the Department of Civil Engineering & Geomatics of the Cyprus University of Technology through coordination and support actions as well as through targeted research activities with the support of European leading institutions. Project tasks aim to fill research multidisciplinary gaps, push, and extend knowledge into new and innovative fields dealing with the monitoring, digitization, visualization, and preservation of ancient monuments and cultural heritage sites, assisting their protection, promotion, and safeguarding

Use of polyethylene glycol coatings for optical fibre humidity sensing

Humidity induced change in the refractive index and thickness of the polyethylene glycol (PEG) coatings are in situ investigated for a range from 10 to 95%, using an optical waveguide spectroscopic technique. It is experimentally demonstrated that, upon humidity change, the optical and swelling characteristics of the PEG coatings can be employed to build a plastic fibre optic humidity sensor. The sensing mechanism is based on the humidity induced change in the refractive index of the PEG film, which is directly coated onto a polished segment of a plastic optical fibre with dip-coating method. It is observed that PEG, which is a highly hydrophilic material, shows no monotonic linear response to humidity but gives different characteristics for various ranges of humidity levels both in index of refraction and in thickness. It undergoes a physical phase change from a semi-crystal line structure to a gel one at around 80% relative humidity. At this phase change point, a drastic decrease occurs in the index of refraction as well as a drastic increase in the swelling of the PEG film. In addition, PEG coatings are hydrogenated in a vacuum chamber. It is observed that the hydrogen has a preventing effect on the humidity induced phase change in PEG coatings. Finally, the possibility of using PEG coatings in construction of a real plastic fibre optic humidity sensor is discussed. (C) 2008 The Optical Society of Japan

Structure from motion photogrammetry in forestry : a review

AbstractPurpose of ReviewThe adoption of Structure from Motion photogrammetry (SfM) is transforming the acquisition of three-dimensional (3D) remote sensing (RS) data in forestry. SfM photogrammetry enables surveys with little cost and technical expertise. We present the theoretical principles and practical considerations of this technology and show opportunities that SfM photogrammetry offers for forest practitioners and researchers.Recent FindingsOur examples of key research indicate the successful application of SfM photogrammetry in forestry, in an operational context and in research, delivering results that are comparable to LiDAR surveys. Reviewed studies have identified possibilities for the extraction of biophysical forest parameters from airborne and terrestrial SfM point clouds and derived 2D data in area-based approaches (ABA) and individual tree approaches. Additionally, increases in the spatial and spectral resolution of sensors available for SfM photogrammetry enable forest health assessment and monitoring. The presented research reveals that coherent 3D data and spectral information, as provided by the SfM workflow, promote opportunities to derive both structural and physiological attributes at the individual tree crown (ITC) as well as stand levels.SummaryWe highlight the potential of using unmanned aerial vehicles (UAVs) and consumer-grade cameras for terrestrial SfM-based surveys in forestry. Offering several spatial products from a single sensor, the SfM workflow enables foresters to collect their own fit-for-purpose RS data. With the broad availability of non-expert SfM software, we provide important practical considerations for the collection of quality input image data to enable successful photogrammetric surveys

Optimized magnetic hysteresis management in numerical electromagnetic field simulations

International audienceThe treatment of hysteresis in numerical simulations represents major issues as large computational times and significant memory space allocations are required. The memory management of the Jiles-Atherton model is simple, but its integration requires relatively fine temporal discretization to achieve convergence. Oppositely, the Preisach model gives satisfactory results with a coarser temporal grid but requires vast memory space and complex management. The Derivative Static Hysteresis Model (DSHM) is an alternative solution for improved performances. The hysteresis law is considered in a generalized input vector space. An interpolation matrix is constructed with the columns and rows denoting the discrete values of H and B and whose terms stand for the dB/dH slope at the corresponding point. Up to now, the filling step of the DSHM matrix has always been through experimental first-order reversal curves, but getting such experimental data is always complex. In this study, we propose to fill the DSHM matrix alternatively. We use simulated first-order reversal curves obtained from the Jiles-Atherton or the Preisach model, which have been identified using limited experimental data (the first magnetization curve and the major hysteresis cycle)