An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds

Turner, Darren; Lucieer, Arko; Watson, CS

doi:10.3390/rs4051392

Cited by 611 publications

(435 citation statements)

References 17 publications

(27 reference statements)

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“…The position (georeferencing) and the attitude (rotation towards the coordinates system) of each acquisition is obtained by estimating the image orientation. In the dense point cloud generation, 3-D point clouds are generated from a set of images, while the orthophoto is generated in the last step, combining the oriented images projected onto the generated point cloud, leading to orthorectified images (Turner et al, 2012). Point clouds can very often be converted into digital surface models (DSMs), and digital terrain models (DTMs) can be extracted by removing the off-ground regions (mainly buildings and trees).…”

Section: Introductionmentioning

confidence: 99%

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Giordan

Hayakawa

Nex

et al. 2018

Nat. Hazards Earth Syst. Sci.

132

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Abstract. The number of scientific studies that consider possible applications of remotely piloted aircraft systems (RPASs) for the management of natural hazards effects and the identification of occurred damages strongly increased in the last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of the literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPASs can acquire on-demand ultra-high-resolution images that can be used for the identification of active processes such as landslides or volcanic activities but can also define the effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazards. IntroductionIn the last three decades, the number of natural disasters showed a positive trend with an increase in the number of affected populations. Disasters not only affected the poor and characteristically more vulnerable countries but also those thought to be better protected. The Annual Disaster Statistical Review describes recent impacts of natural disasters on the population and reports 342 naturally triggered disasters in 2016 (Guha-Sapir et al., 2017). This is less than the annual average disaster frequency observed from 2006 to 2015 (376.4 events). However, natural disasters are still responsible for a high number of casualties (8733 death). In the period 2006-2015, the average number of causalities caused annually by natural disasters is 69 827. In 2016, hydrological disasters (177) had the largest share in natural disaster occurrence (51.8 %), followed by meteorological disasters (96; 28.1 %), climatological disasters (38; 11.1 %) and geophysical disasters (31; 9.1 %) (Guha-Sapir et al., 2017). To face these disasters, one of the most important solutions is the use of systems able to provide an adequate level of information for correctly understanding these events and their evolution. In this context, surveying and monitoring natural hazards gained importance. In particular, during the emergency phase it is very important to evaluate and control the phenomenon of evolution, preferably operating in near real time or real time, and consequently, use this information for a better risk assessment scenario. The available acquired data must be processed rapidly to support the emergency services and decision makers.Recently, the use of remote sensing (satellite and airborne platform) in the field of natural hazards and disasters has become common, also supported by the increase in geospatial technologies and the ability to provide and process up-to-date imagery (Joyce et al., 2009;Tarolli, 2014). Remotely sensed data play an integral role in predicting hazard events such as floods and landslides, subsidence events and other ground instabilities. Because of their acquisition mode and capabilPublished by Copern...

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Section: Introductionmentioning

confidence: 99%

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Giordan

Hayakawa

Nex

et al. 2018

Nat. Hazards Earth Syst. Sci.

132

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“…A terrain model was generated from the selected images using SFM technology. The validity of this approach is verified by [27] and [21] in providing satisfactory results Four skid trail sections of approx. 50 m in length were selected for detailed analysis.…”

Section: Data Acquisitionmentioning

confidence: 80%

“…While the software automatically calculates camera calibration parameters from EXIF (exchangeable image file format) metadata, it is possible to fit camera parameters in order to minimize the model distortion known as "bowl effect" using ground control points as a reference in a process known as camera optimization [32]. Geo-referencing is performed after a sparse point cloud is reconstructed, and done using ground control points where the whole model is transformed to the preselected coordinate system using Helmert transformation .This method is called ground control point (GCP) technique [21]. Based on the estimated camera positions the program calculates depth information based on the intersection of light rays between different camera stations, to be combined into a single dense point cloud.…”

Section: Data Processingmentioning

confidence: 99%

“…Images from UAV's mostly come from consumer-grade cameras, not initially designed for metric purposes, which is why interior orientation (IO) parameters are usually not known or inaccurate. Also, exterior camera orientation (EO) is less accurate for UAV platforms due to limited accuracy GPS and the Micro Electro Mechanical System's (MEMS) Inertial Measurement Unit (IMU) [21]. In order to compensate for these shortcomings a sophisticated processing workflow is applied using Structure from Motion (SFM).…”

Section: Introductionmentioning

confidence: 99%

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Estimating Soil Displacement from Timber Extraction Trails in Steep Terrain: Application of an Unmanned Aircraft for 3D Modelling

Pierzchała¹,

Talbot²,

Astrup³

2014

Forests

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Skid trails constructed for timber extraction in steep terrain constitute a serious environmental concern if not well planned, executed and ameliorated. Carrying out post-harvest surveys in monitoring constructed trails in such terrain is an onerous task for forest administrators, as hundreds of meters need to be surveyed per site, and the quantification of parameters and volumes is largely based on assumptions of trail symmetry and terrain uniformity. In this study, aerial imagery captured from a multi-rotor Unmanned Aerial Vehicle was used in generating a detailed post-harvest terrain model which included all skid trails. This was then compared with an Airborne Laser Scanning derived pre-harvest terrain model and the dimensions, slopes and cut-and-fill volumes associated with the skid trails were determined. The overall skid trail length was 954 m, or 381 m·ha −1 with segments varying from 40-60 m, inclinations from 3.9% to 9.6%, and cut volumes, from 1.7 to 3.7 m 3 per running meter. The methods used in this work can be used in rapidly assessing the extent of disturbance and erosion risk on a wide range of sites. The multi-rotor Unmanned Aerial Vehicle (UAV) was found to be highly suited to the task, given the relatively small size of harvested stands, their shape and their location in the mountainous terrain.

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“…Image mosaics are among the most important targeted products of an aerial imaging system where the individual acquired images during the flight mission is stitched together to offer a large field of view of the surveyed scene. Image stitching/mosaicing has gained a lot of attention in many applications such as medical imagery (Can et al, 1999;Choe et al, 2006;Usmani et al, 2014), navigation (Lucas et al, 2010), and remote sensing (Turner et al, 2012;Wei et al, 2015). For constructing a stitched image, the transformations between the involved images have to be estimated based on overlapping areas between these images.…”

Section: Introductionmentioning

confidence: 99%

A Fast Approach for Stitching of Aerial Images

Moussa

El‐Sheimy

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs). These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT) algorithm, the proposed approach uses the initial image's coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre-estimated transformation parameters of the images are employed successively in a growing fashion to create the stitched image and the coverage image. The proposed approach is implemented and tested using the images acquired through a UAV flight mission and the achieved results are presented and discussed.

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An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds

Cited by 611 publications

References 17 publications

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Estimating Soil Displacement from Timber Extraction Trails in Steep Terrain: Application of an Unmanned Aircraft for 3D Modelling

A Fast Approach for Stitching of Aerial Images

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