Direct Georeferencing Research Articles

UAV DIRECT GEOREFERENCING APPROACH IN AN EMERGENCY MAPPING CONTEXT. THE 2016 CENTRAL ITALY EARTHQUAKE CASE STUDY

Abstract. UAVs platform are increasingly deployed by first responders and local stakeholders to get a first overview of disaster affected areas with a high level of detail and different off-nadir angle configurations. Through a rapid mapping approach, the acquired data (video sequences or pictures) are analysed to extract information on damages to buildings and infrastructures with the goal to support the Search and Rescue operations. The specific focus of the paper is on evaluating the expected benefits (from the rapid mapping perspective) deriving from a direct georeferencing approach when using UAV with RTK capabilities. Specifically, data acquired by a fixed wing eBee RTK platform by SenseFly over the areas affected by the earthquake that hit central Italy in 2016 have been processed to compare the positional accuracies of orthoimagery generated by means of a direct georeferencing approach (without any GPC) with and without a post-processing kinematic solution. The results highlight that an RTK-enabled platform allows to achieve orthoimagery positioning accuracy values up to few centimeters without the need of any control point. In the conclusion session the operational implications of a PPK-based approach versus a standard direct georeferencing are critically discussed.

COMPARISON OF UAV LIDAR AND IMAGERY FOR BEACH MONITORING

Abstract. With recent advancements in UAV based technology the use of airborne photogrammetry and LiDAR poses a new and effective approach for continuous, fast and efficient beach monitoring surveys. This paper aims to compare three platforms (a DJI Phantom Pro 4 using Ground Control Points, a DJI Matrice 200 with built in PPK allowing direct georeferencing and a DJI Matrice 600 with a Riegl Mini-VUX LiDAR system) in order to assess if they enable beach surveys to be performed efficiently, accurately and cost- effectively. A series of beach surveys were performed over a period of 6 months enabling the ability of each UAV surveying technique to be assessed for the identification and evaluation of trends in the changing topography of beaches and shorelines. The study area (Warnbro Sound, Western Australia) is an area that has experienced significant coastal change over the last 20 years as well as several serious weather events in the course of this research. The results show a significant positive bias of a consistent vertical offset to the ground surface by 4–9 cm between the two image based systems in comparison to the LiDAR system. Although these height offsets are significant it is still within the accuracy required to perform successful beach surveys, and all systems were able to quantify the change of the beach shoreline in area (m2) and volume (m3).

TREE SPECIES CLASSIFICATION BASED ON 3D SPECTRAL POINT CLOUDS AND ORTHOMOSAICS ACQUIRED BY SNAPSHOT HYPERSPECTRAL UAS SENSOR

Abstract. In recent years, there has been a growing number of small hyperspectral sensors suitable for deployment on unmanned aerial systems (UAS. The introduction of the hyperspectral snapshot sensor provides interesting opportunities for acquisition of three-dimensional (3D) hyperspectral point clouds based on the structure-from-motion (SfM) workflow. In this study, we describe the integration of a 25-band hyperspectral snapshot sensor (PhotonFocus camera with IMEC 600 – 875 nm 5x5 mosaic chip) on a multi-rotor UAS. The sensor was integrated with a dual frequency GNSS receiver for accurate time synchronisation and geolocation. We describe the sensor calibration workflow, including dark current and flat field characterisation. An SfM workflow was implemented to derive hyperspectral 3D point clouds and orthomosaics from overlapping frames. On-board GNSS coordinates for each hyperspectral frame assisted in the SfM process and allowed for accurate direct georeferencing (< 10 cm absolute accuracy). We present the processing workflow to generate seamless hyperspectral orthomosaics from hundreds of raw images. Spectral reference panels and in-field spectral measurements were used to calibrate and validate the spectral signatures. This process provides a novel data type which contains both 3D, geometric structure and detailed spectral information in a single format. First, to determine the potential improvements that such a format could provide, the core aim of this study was to compare the use of 3D hyperspectral point clouds to conventional hyperspectral imagery in the classification of two Eucalyptus tree species found in Tasmania, Australia. The IMEC SM5x5 hyperspectral snapshot sensor was flown over a small native plantation plot, consisting of a mix of the Eucalyptus pauciflora and E. tenuiramis species. High overlap hyperspectral imagery was captured and then processed using SfM algorithms to generate both a hyperspectral orthomosaic and a dense hyperspectral point cloud. Additionally, to ensure the optimum spectral quality of the data, the characteristics of the hyperspectral snapshot imaging sensor were analysed utilising measurements captured in a laboratory environment. To coincide with the generated hyperspectral point cloud data, both a file format and additional processing and visualisation software were developed to provide the necessary tools for a complete classification workflow. Results based on the classification of the E. pauciflora and E. tenuiramis species revealed that the hyperspectral point cloud produced an increased classification accuracy over conventional hyperspectral imagery based on random forest classification. This was represented by an increase in classification accuracy from 67.2% to 73.8%. It was found that even when applied separately, the geometric and spectral feature sets from the point cloud both provided increased classification accuracy over the hyperspectral imagery.

Towards Automatic UAS-Based Snow-Field Monitoring for Microclimate Research.

This article presents unmanned aerial system (UAS)-based photogrammetry as an efficient method for the estimation of snow-field parameters, including snow depth, volume, and snow-covered area. Unlike similar studies employing UASs, this method benefits from the rapid development of compact, high-accuracy global navigation satellite system (GNSS) receivers. Our custom-built, multi-sensor system for UAS photogrammetry facilitates attaining centimeter- to decimeter-level object accuracy without deploying ground control points; this technique is generally known as direct georeferencing. The method was demonstrated at Mapa Republiky, a snow field located in the Krkonose, a mountain range in the Czech Republic. The location has attracted the interest of scientists due to its specific characteristics; multiple approaches to snow-field parameter estimation have thus been employed in that area to date. According to the results achieved within this study, the proposed method can be considered the optimum solution since it not only attains superior density and spatial object accuracy (approximately one decimeter) but also significantly reduces the data collection time and, above all, eliminates field work to markedly reduce the health risks associated with avalanches.

Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control

Mapping landfast sea ice at a fine spatial scale is not only meaningful for geophysical study, but is also of benefit for providing information about human activities upon it. The combination of unmanned aerial systems (UAS) with structure from motion (SfM) methods have already revolutionized the current close-range Earth observation paradigm. To test their feasibility in characterizing the properties and dynamics of fast ice, three flights were carried out in the 2016–2017 austral summer during the 33rd Chinese National Antarctic Expedition (CHINARE), focusing on the area of the Prydz Bay in East Antarctica. Three-dimensional models and orthomosaics from three sorties were constructed from a total of 205 photos using Agisoft PhotoScan software. Logistical challenges presented by the terrain precluded the deployment of a dedicated ground control network; however, it was still possible to indirectly assess the performance of the photogrammetric products through an analysis of the statistics of the matching network, bundle adjustment, and Monte-Carlo simulation. Our results show that the matching networks are quite strong, given a sufficient number of feature points (mostly > 20,000) or valid matches (mostly > 1000). The largest contribution to the total error using our direct georeferencing approach is attributed to inaccuracies in the onboard position and orientation system (POS) records, especially in the vehicle height and yaw angle. On one hand, the 3D precision map reveals that planimetric precision is usually about one-third of the vertical estimate (typically 20 cm in the network centre). On the other hand, shape-only errors account for less than 5% for the X and Y dimensions and 20% for the Z dimension. To further illustrate the UAS’s capability, six representative surface features are selected and interpreted by sea ice experts. Finally, we offer pragmatic suggestions and guidelines for planning future UAS-SfM surveys without the use of ground control. The work represents a pioneering attempt to comprehensively assess UAS-SfM survey capability in fast ice environments, and could serve as a reference for future improvements.

Modelling glacier topography in Antarctica using unmanned aerial survey: assessment of opportunities

ABSTRACTWe discuss the application of unmanned aerial systems (UASs) and UAS-derived data to model glacier topography for detecting and analysing slow and rapid changes in glacier surfaces. The study was conducted in East Antarctica in the austral summer 2016/2017. The surveyed areas included an eastern part of the Larsemann Hills, an Airfield of the Progress Station, an initial section of a Sledge route from the Progress to Vostok Stations, and a north-western margin of the Dålk Glacier. We used a Geoscan 201 Geodesy, a professional-grade flying-wing UAS. For the photogrammetric processing, we utilized the Agisoft PhotoScan software. The direct georeferencing approach was applied. Several high-resolution, multitemporal digital elevation models (DEMs) for surveyed areas were produced. The DEM accuracy was estimated in the absence of ground control points and reference DEMs. We determined the ice flow velocity within the Airfield area and the Sledge route zone, where marked points were installed and moved together with the ice surface. We found that UAS imagery can be used for real-time monitoring of open and some snow-covered crevasses of various sizes. Wind-driven snow microforms are also well recognizable on UAS-derived aerial images. The 2017 Dålk Glacier catastrophic subsidence demonstrated that monitoring and studying such events in glaciers and ice sheets are almost impossible without UASs. Optimal meteorological conditions were empirically determined for conducting unmanned aerial survey to obtain images suitable for subsequent photogrammetric processing and DEM generation. Finally, we discuss adaptation of equipment and software to the Antarctic environment.

An approach to improve direct sensor orientation using the integration of photogrammetric and lidar datasets

ABSTRACT The Direct Sensor Georeferencing (DSG) can co-register both photogrammetric and light detection and ranging (lidar) datasets in a common mapping reference system. This procedure is based on the information (position and orientation) derived from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. Due to inaccurate system mounting parameters, the attitudes and positions are badly calculated. In this scenario, the accuracy of the boresight misalignment angles (physical mounting angles between digital camera and IMU) is critical to obtain models free of y-parallaxes. Therefore, a system calibration procedure is required to improve DSG using photogrammetric and lidar datasets. In this sense, this article aims to develop an approach to improve DSG based on the enhancements of the accuracies of the photogrammetric and lidar datasets integration. For this purpose, one sub-block was extracted at the centre of the entire block. Then, the in situ camera calibration was performed using the sub-block and sets of Lidar Control Points. Sequentially, a boresight calibration was performed using Virtual Lidar Control Points. The quality assessment of the approach was measured in both image and object spaces, using two-ray checkpoints at the entire block. The overall results from the performed experiments showed significant improvements of the Root Mean Square Error of the checkpoint discrepancies computed by direct georeferencing.

Adaptive Re-weighted Block Adjustment for Multi-Coverage Satellite Stereo Images without Ground Control Points

Georeferencing satellite imagery is the basis for mapping and other subsequent processing in photogrammetry and remote sensing. Traditional georeferencing methods, which require adequate and well-distributed ground control points (GCPs), are time-consuming, costly, constantly cumbersome and inefficient. This study proposes a method of adaptive re-weighted block adjustment without GCPs for satellite stereo images. Furthermore, an approach of using multi-coverage images to improve the direct georeferencing accuracy is also investigated. The bias-compensated rational function model with affine transformation parameters is used as the adjustment mathematical model. The “weighted virtual control points” are introduced as constraints in the adjustment. Space intersection and resection are executed iteratively to solve the accurate affine transformation parameters. A total of 722 three-line array stereo scenes of Chinese Mapping Satellite-1 images captured from December 2010 to March 2016, which cover the entire Shandong Province of China (about 158,000 km2) nearly 10 times in space, are used as experimental data to evaluate the performance of the proposed method. Experiment results demonstrate that the proposed method can effectively eliminate the systematic errors of images. Moreover, the additional comparison with experiments of three groups of mono-coverage images indicates that the redundant observations from multi-coverage images can significantly improve the direct georeferencing accuracy, more specifically, from 13.69–16.02 m to 11.09 m in the horizontal direction, and from 16.39–29.27 m to 8.47 m in the vertical direction.

Comparison of four UAV georeferencing methods for environmental monitoring purposes focusing on the combined use with airborne and satellite remote sensing platforms

This work is aimed at the environmental remote sensing community that uses UAV optical frame imagery in combination with airborne and satellite data. Taking into account the economic costs involved and the time investment, we evaluated the fit-for-purpose accuracy of four positioning methods of UAV-acquired imagery: 1) direct georeferencing using the onboard raw GNSS (GNSSNAV) data, 2) direct georeferencing using Post-Processed Kinematic single-frequency carrier-phase without in situ ground support (PPK1), 3) direct georeferencing using Post-Processed Kinematic double-frequency carrier-phase GNSS data with in situ ground support (PPK2), and 4) indirect georeferencing using Ground Control Points (GCP). We tested a multispectral sensor and an RGB sensor, onboard multicopter platforms. Orthophotomosaics at <0.05 m spatial resolution were generated with photogrammetric software. The UAV image absolute accuracy was evaluated according to the ASPRS standards, wherein we used a set of GCPs as reference coordinates, which we surveyed with a differential GNSS static receiver. The raw onboard GNSSNAV solution yielded horizontal (radial) accuracies of RMSEr≤1.062 m and vertical accuracies of RMSEz≤4.209 m; PPK1 solution gave decimetric accuracies of RMSEr≤0.256 m and RMSEz≤0.238 m; PPK2 solution, gave centimetric accuracies of RMSEr≤0.036 m and RMSEz≤0.036 m. These results were further improved by using the GCP solution, which yielded accuracies of RMSEr≤0.023 m and RMSEz≤0.030 m. GNSSNAV solution is a fast and low-cost option that is useful for UAV imagery in combination with remote sensing products, such as Sentinel-2 satellite data. PPK1, which can register UAV imagery with remote sensing products up to 0.25 m pixel size, as WorldView-like satellite imagery, airborne lidar or orthoimagery, has a higher economic cost than the GNSSNAV solution. PPK2 is an acceptable option for registering remote sensing products of up to 0.05 m pixel size, as with other UAV images. Moreover, PPK2 can obtain accuracies that are approximate to the usual UAV pixel size (e.g. co-register in multitemporal studies), but it is more expensive than PPK1. Although indirect georeferencing can obtain the highest accuracy, it is nevertheless a time-consuming task, particularly if many GCPs have to be placed. The paper also provides the approximate cost of each solution.

KINEMATIC CALIBRATION USING LOW-COST LiDAR SYSTEM FOR MAPPING AND AUTONOMOUS DRIVING APPLICATIONS

Abstract. More recently, mapping sensors for land-based Mobile Mapping Systems (MMSs) have combined cameras and laser scanning measurements defined as Light Detection and Ranging (LiDAR), or laser scanner together. These mobile laser scanning systems (MLS) can be used in dynamic environments and are able of being adopted in traffic-related applications, such as the collection of road network databases, inventory of traffic sign and surface conditions, etc. However, most LiDAR systems are expensive and not easy to access. Moreover, due to the increasing demand of the autonomous driving system, the low-cost LiDAR systems, such as Velodyne or SICK, have become more and more popular these days. These kinds of systems do not provide the total solution. Users need to integrate with Inertial Navigation System/ Global Navigation Satellite System (INS/GNSS) or camera by themselves to meet their requirement. The transformation between LiDAR and INS frames must be carefully computed ahead of conducting direct geo-referencing. To solve these issues, this research proposes the kinematic calibration model for a land-based INS/GNSS/LiDAR system. The calibration model is derived from the direct geo-referencing model and based on the conditioning of target points where lie on planar surfaces. The calibration parameters include the boresight and lever arm as well as the plane coefficients. The proposed calibration model takes into account the plane coefficients, laser and INS/GNSS observations, and boresight and lever arm. The fundamental idea is the constraint where geo-referenced point clouds should lie on the same plane through different directions during the calibration. After the calibration process, there are two evaluations using the calibration parameters to enhance the performance of proposed applications. The first evaluation focuses on the direct geo-referencing. We compared the target planes composed of geo- referenced points before and after the calibration. The second evaluation concentrates on positioning improvement after taking aiding measurements from LiDAR- Simultaneously Localization and Mapping (SLAM) into INS/GNSS. It is worth mentioning that only one or two planes need to be adopted during the calibration process and there is no extra arrangement to set up the calibration field. The only requirement for calibration is the open sky area with the clear plane construction, such as wall or building. Not only has the contribution in MMSs or mapping, this research also considers the self-driving applications which improves the positioning ability and stability.

TERRAIN AWARE IMAGE CLIPPING FOR REAL-TIME AERIAL MAPPING

Abstract. Many remote sensing applications demand for a fast and efficient way of generating orthophoto maps from raw aerial images. One prerequisite is direct georeferencing, which allows to geolocate aerial images to their geographic position on the earth’s surface. But this is only half the story. When dealing with a large quantity of highly overlapping images, a major challenge is to select the most suitable image parts in order to generate seamless aerial maps of the captured area. This paper proposes a method that quickly determines such an optimal (rectangular) section for each single aerial image, which in turn can be used for generating seamless aerial maps. Its key approach is to clip aerial images depending on their geometric intersections with a terrain elevation model of the captured area, which is why we call it terrain aware image clipping (TAC). The method has a modest computational footprint and is therefore applicable even for rather limited embedded vision systems. It can be applied for both, real-time aerial mapping applications using data links as well as for rapid map generation right after landing without any postprocessing step. Referring to real-time applications, this method also minimizes transmission of redundant image data. The proposed method has already been demonstrated in several search-and-rescue scenarios and real-time mapping applications using a broadband data link and diffent kinds of camera and carrier systems. Moreover, a patent for this technology is pending.

Accurate georeferencing of TLS point clouds with short GNSS observation durations even under challenging measurement conditions

Abstract The accuracy of georeferenced TLS point clouds is directly influenced by site-dependent GNSS effects, deteriorating the accuracy of the ground control point coordinate estimation. Especially under challenging GNSS conditions, this is a crucial problem. One common approach is to minimize these effects by longer observation durations, which in turn increases the effort in time and cost. In this paper, an algorithm is proposed that provides accurate georeferencing results, even under challenging measurement conditions and short observation durations. It iteratively improves the georeferencing accuracy by determining and applying obstruction adaptive elevation masks to the GNSS observations. The algorithm is tested and assessed using the data of a field test. It is demonstrated that after only 5 minutes observation duration, the ground control point coordinates can be estimated with an accuracy of 1 to 2 cm, independent from the GNSS measurement conditions. Initial states of the elevation masks are determined from a point cloud that is georeferenced using coordinates from a single point positioning solution, enhanced by a RAIM-FDE approach. Afterwards, the coordinates are estimated in a weighted least-squares baseline solution and both, the elevation masks and the coordinate estimation, are iteratively improved. Besides the significant reduction of measurement time, the proposed algorithm allows for increasing the amount of ground control points and can be applied to other direct or indirect GNSS-based georeferencing approaches.

REAL-TIME AND POST-PROCESSED GEOREFERENCING FOR HYPERPSPECTRAL DRONE REMOTE SENSING

Abstract. The use of drones and photogrammetric technologies are increasing rapidly in different applications. Currently, drone processing workflow is in most cases based on sequential image acquisition and post-processing, but there are great interests towards real-time solutions. Fast and reliable real-time drone data processing can benefit, for instance, environmental monitoring tasks in precision agriculture and in forest. Recent developments in miniaturized and low-cost inertial measurement systems and GNSS sensors, and Real-time kinematic (RTK) position data are offering new perspectives for the comprehensive remote sensing applications. The combination of these sensors and light-weight and low-cost multi- or hyperspectral frame sensors in drones provides the opportunity of creating near real-time or real-time remote sensing data of target object. We have developed a system with direct georeferencing onboard drone to be used combined with hyperspectral frame cameras in real-time remote sensing applications. The objective of this study is to evaluate the real-time georeferencing comparing with post-processing solutions. Experimental data sets were captured in agricultural and forested test sites using the system. The accuracy of onboard georeferencing data were better than 0.5 m. The results showed that the real-time remote sensing is promising and feasible in both test sites.

Using RTK and VRS in direct geo-referencing of the UAV imagery

Direct Geo-Referencing is a new technique in photogrammetry, especially in the aerial photogrammetry. Unlike the Aerial Triangulation “AT”, this method does not require Ground Control Points “GCPs”, to process aerial photographs into desired ground coordinates systems. Compared with the old method, this method has four main advantages: faster field work, faster data processing, simple workflow and less cost. Generally, direct geo-referencing using two systems, Global Navigational Satellite Systems “GNSS” and Inertial Navigational System “INS”. GNSS recording the camera coordinates “X, Y, Z”, and INS recording the camera orientation angles “w, φ, k” at the time of exposure. These parameters merged and are provided to each photograph in the processing stage.The current paper investigates the using GNSS system for providing the linear exterior orientation “EO” parameters “X, Y, Z” by two techniques, real time kinematic “RTK” and virtual reference system “VRS”. The accuracy of the applied method is tested on topographic survey project in Switzerland. The surveyed data of the specified project were collected by amateur digital camera Canon 18.2 MP, 182 captured images from approximately 85 m flight height, 18 Ground Check Point “GCP” determined by static GNSS. Horizontal accuracy is 0.029 m for VRS case, 0.034 m for RTK case and vertical accuracy is 0.026 m for VRS case, 0.029 for RTK case.

Boresight Calibration of GNSS/INS-Assisted Push-Broom Hyperspectral Scanners on UAV Platforms

Low-cost unmanned aerial vehicles (UAVs) utilizing push-broom hyperspectral scanners are poised to become a popular alternative to conventional remote sensing platforms such as manned aircraft and satellites. In order to employ this emerging technology in fields such as high-throughput phenotyping and precision agriculture, direct georeferencing of hyperspectral data using onboard integrated global navigation satellite systems (GNSSs) and inertial navigation systems (INSs) is required. Directly deriving the scanner position and orientation requires the spatial and rotational relationship between the coordinate systems of the GNSS/INS and hyperspectral scanner to be measured. The spatial offset (lever arm) between the scanner and GNSS/INS unit can be measured manually. However, the angular relationship (boresight angles) between the scanner and GNSS/INS coordinate systems, which is more critical for accurate generation of georeferenced products, is difficult to establish. This paper presents three calibration approaches to estimate the boresight angles relating hyperspectral push-broom scanner and GNSS/INS coordinate systems. For reliable/practical estimation of the boresight angles, this paper starts with establishing the optimal/minimal flight and control/tie point configuration through a bias impact analysis starting from the point positioning equation. Then, an approximate calibration procedure utilizing tie points in overlapping scenes is presented after making some assumptions about the flight trajectory and topography of covered terrain. Next, two rigorous approaches are introduced – one using ground control points and other using tie features. The approximate/rigorous approaches are based on enforcing the collinearity and coplanarity of the light rays connecting the perspective centers of the imaging scanner, object point, and the respective image points. To evaluate the accuracy of the proposed approaches, estimated boresight angles are used for orthorectification of six hyperspectral UAV dataset acquired over an agricultural field. Qualitative and quantitative evaluations of the results have shown significant improvement in the derived orthophotos to a level equivalent to the ground sampling distance of the used scanner (namely, 3–5 cm when flying at 60 m).

Pushbroom Hyperspectral Data Orientation by Combining Feature-Based and Area-Based Co-Registration Techniques

Direct georeferencing of airborne pushbroom scanner data usually suffers from the limited precision of navigation sensors onboard of the aircraft. The bundle adjustment of images and orientation parameters, used to perform geocorrection of frame images during the post-processing phase, cannot be used for pushbroom cameras without difficulties—it relies on matching corresponding points between scan lines, which is not feasible in the absence of sufficient overlap and texture information. We address this georeferencing problem by equipping our aircraft with both a frame camera and a pushbroom scanner: the frame images and the navigation parameters measured by a couple GPS/Inertial Measurement Unit (IMU) are input to a bundle adjustment algorithm; the output orientation parameters are used to project the scan lines on a Digital Elevation Model (DEM) and on an orthophoto generated during the bundle adjustment step; using the image feature matching algorithm Speeded Up Robust Features (SURF), corresponding points between the image formed by the projected scan lines and the orthophoto are matched, and through a least-squares method, the boresight between the two cameras is estimated and included in the calculation of the projection. Finally, using Particle Image Velocimetry (PIV) on the gradient image, the projection is deformed into a final image that fits the geometry of the orthophoto. We apply this algorithm to five test acquisitions over Lake Geneva region (Switzerland) and Lake Baikal region (Russia). The results are quantified in terms of Root Mean Square Error (RMSE) between matching points of the RGB orthophoto and the pushbroom projection. From a first projection where the Interior Orientation Parameters (IOP) are known with limited precision and the RMSE goes up to 41 pixels, our geocorrection estimates IOP, boresight and Exterior Orientation Parameters (EOP) and produces a new projection with an RMSE, with the reference orthophoto, around two pixels.

Exploración Multisensor para la Detección de Anomalías del Terreno y Análisis Espectral En y Bajo Superficie

Se ha contribuido en los últimos años desde distintos organismos y centros de investigación al desarrollo de sistemas multisensor orientados a diversas aplicaciones en el segmento civil con potencialidades en la exploración tridimensional del terreno a efectos de localización de evidencias, restos materiales en o bajo superficie. Dichos diseños permiten realizar una inspección completa en el rango del espectro electromágnético del visible e infrarrojo cercano al infrarrojo térmico, capacitando su instalación en diversas plataformas aéreas y terrestres. El sistema dota e integra dispositivos de navegación y orientación (GNSS/INS) lo que posibilita la georeferenciación directa y precisa de la información obtenida. La propuesta de exploración se realiza mediante un radar laser aéreo (Lídar) para la obtención de nubes de puntos 3D, una estructura inclinable lateralmente con tres cámaras visibles de alta definición orientadas para un apuntamiento multiángulo configurable, radio-altímetro de ondas milimétricas y una cabeza giroestabilizada con doble cámara multiespectral en el visible e infrarrojo próximo y térmico. La información adquirida sobre diferentes escenarios es explotada de manera automática mediante métodos y algoritmos propios de teledetección y tratamiento de datos. Entre ellos se propone una nueva técnica de detección a partir de espectrometría de imagen de pequeños objetivos infrarrojos basada en la búsqueda de la mejor proyección de valores fuera de rango sobre un fondo térmico. Este método aporta unos resultados altamente correlacionados con diversas entidades lo que posibilita una prospección automática del terreno, identificación de objetivos potenciales y localización de evidencias en investigaciones científicas sobre diferentes escenarios.

Calibration and accuracy assessment in a direct georeferencing system for UAS photogrammetry

ABSTRACTUnmanned aerial systems (UASs) have been already proven to be useful in fields and disciplines such as agriculture, forestry, or environmental mapping, and they have also found application during natural and nuclear disasters. In many cases, the environment is inaccessible or dangerous for a human being, meaning that the widely used technique of aerial imagery georeferencing via ground control points cannot be employed. The present article introduces a custom-built multi-sensor system for direct georeferencing, a concept that enables georeferencing to be performed without an access to the mapping area and ensures centimetre-level object accuracy. The proposed system comprises leading navigation system technologies in the weight category of micro and light UASs. A highly accurate Global Navigation Satellite System receiver integrating the real-time kinematic technology supports an inertial navigation system, where data from various sensors are fused. Special attention is paid to the time synchronization of all sensors, and a method for the field calibration of the system is designed. The multi-sensor system is completely independent of the used UASs. The authors also discuss the verification of the proposed system’s performance on a real mission. To make the results credible, a high number of test points are used, with both direct and indirect georeferencing techniques subjected to comparison, together with different calibration methods. The achieved spatial object accuracy (about 4 cm root mean square error (RMSE)) is sufficient for most applications.

Direct Georeferencing of a Pushbroom, Lightweight Hyperspectral System for Mini-UAV Applications

Hyperspectral imagery has proven its potential in many research applications, especially in the field of environmental sciences. Currently, hyperspectral imaging is generally performed by satellite or aircraft platforms, but mini-UAV (Unmanned Aerial Vehicle) platforms (&lt;20 kg) are now emerging. On such platforms, payload restrictions are critical, so sensors must be selected according to stringent specifications. This article presents the integration of a light pushbroom hyperspectral sensor onboard a multirotor UAV, which we have called Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations). This article depicts the system design: the UAV platform, the imaging module, the navigation module, and the interfacing between the different elements. Pushbroom sensors offer a better combination of spatial and spectral resolution than full-frame cameras. Nevertheless, data georectification has to be performed line by line, the quality of direct georeferencing being limited by mechanical stability, good timing accuracy, and the resolution and accuracy of the proprioceptive sensors. A georegistration procedure is proposed for geometrical pre-processing of hyperspectral data. The specifications of Hyper-DRELIO surveys are described through two examples of surveys above coastal or inland waters, with different flight altitudes. This system can collect hyperspectral data in VNIR (Visible and Near InfraRed) domain above small study sites (up to about 4 ha) with both high spatial resolution (&lt;10 cm) and high spectral resolution (1.85 nm) and with georectification accuracy on the order of 1 to 2 m.

Robotic photosieving from low‐cost multirotor sUAS: a proof‐of‐concept

AbstractMeasurement of riverbed material grain sizes is now a routine part of fieldwork in fluvial geomorphology and lotic ecology. In the last decade, several authors have proposed remote sensing approaches of grain size measurements based on terrestrial and aerial imagery. Given the current rise of small unmanned aerial system (sUAS) applications in geomorphology, there is now increasing interest in the application of these remotely sensed grain size mapping methods to sUAS imagery. However, success in this area has been limited owing to two fundamental problems: lack of constraint of image scale for sUAS imagery and blurring effects in sUAS images and resulting orthomosaics. In this work, we solve the former by showing that SfM‐photogrammetry can be used in a direct georeferencing (DG) workflow (i.e. with no ground validation) in order to predict image scale within margins of 3%. We then propose a novel approach of robotic photosieving of dry exposed riverbed grains that relies on near‐ground images acquired from a low‐cost sUAS and which does not require the presence of ground control points or visible scale objects. We demonstrate that this absence of scale objects does not affect photosieving outputs thus resulting in a low‐cost and efficient sampling method for surficial grains. Copyright © 2017 John Wiley &amp; Sons, Ltd.