Automatic Aerial Triangulation Research Articles

COMPARISON OF UAV IMAGERY-DERIVED POINT CLOUD TO TERRESTRIAL LASER SCANNER POINT CLOUD

Abstract. A small unmanned aerial vehicle (UAV) with survey-grade GNSS positioning is used to produce a point cloud for topographic mapping and 3D reconstruction. The objective of this study is to assess the accuracy of a UAV imagery-derived point cloud by comparing a point cloud generated by terrestrial laser scanning (TLS). Imagery was collected over a 320 m by 320 m area with undulating terrain, containing 80 ground control points. A SenseFly eBee Plus fixed-wing platform with PPK positioning with a 10.6 mm focal length and a 20 MP digital camera was used to fly the area. Pix4Dmapper, a computer vision based commercial software, was used to process a photogrammetric block, constrained by 5 GCPs while obtaining cm-level RMSE based on the remaining 75 checkpoints. Based on results of automatic aerial triangulation, a point cloud and digital surface model (DSM) (2.5 cm/pixel) are generated and their accuracy assessed. A bias less than 1 pixel was observed in elevations from the UAV DSM at the checkpoints. 31 registered TLS scans made up a point cloud of the same area with an observed horizontal root mean square error (RMSE) of 0.006m, and negligible vertical RMSE. Comparisons were made between fitted planes of extracted roof features of 2 buildings and centreline profile comparison of a road in both UAV and TLS point clouds. Comparisons showed an average +8 cm bias with UAV point cloud computing too high in two features. No bias was observed in the roof features of the southernmost building.

High-resolution imagery acquired from an unmanned platform to estimate biophysical and geometrical parameters of olive trees under different irrigation regimes.

The experiments were conducted in a fully-productive olive orchard (cv. Frantoio) at the experimental farm of University of Pisa at Venturina (Italy) in 2015 to assess the ability of an unmanned aerial vehicle (UAV) equipped with RGB-NIR cameras to estimate leaf area index (LAI), tree height, canopy diameter and canopy volume of olive trees that were either irrigated or rainfed. Irrigated trees received water 4–5 days a week (1348 m3 ha-1), whereas the rainfed ones received a single irrigation of 19 m3 ha-1 to relieve the extreme stress. The flight altitude was 70 m above ground level (AGL), except for one flight (50 m AGL). The Normalized Difference Vegetation Index (NDVI) was calculated by means of the map algebra technique. Canopy volume, canopy height and diameter were obtained from the digital surface model (DSM) obtained through automatic aerial triangulation, bundle block adjustment and camera calibration methods. The NDVI estimated on the day of the year (DOY) 130 was linearly correlated with both LAI and leaf chlorophyll measured on the same date (R2 = 0.78 and 0.80, respectively). The correlation between the on ground measured canopy volumes and the ones by the UAV-RGB camera techniques yielded an R2 of 0.71–0.86. The monthly canopy volume increment estimated from UAV surveys between (DOY) 130 and 244 was highly correlated with the daily water stress integral of rainfed trees (R2 = 0.99). The effect of water stress on the seasonal pattern of canopy growth was detected by these techniques in correspondence of the maximum level of stress experienced by the rainfed trees. The highest level of accuracy (RMSE = 0.16 m) in canopy height estimation was obtained when the flight altitude was 50 m AGL, yielding an R2 value of 0.87 and an almost 1:1 ratio of measured versus estimated canopy height.

Determination of Shift/Bias in Digital Aerial Triangulation of UAV Imagery Sequences

Currently UAV Photogrammetry is characterized a largely automated and efficient data processing. Depicting from the low altitude more often gains on the meaning in the uses of applications as: cities mapping, corridor mapping, road and pipeline inspections or mapping of large areas e.g. forests. Additionally, high-resolution video image (HD and bigger) is more often use for depicting from the low altitude from one side it lets deliver a lot of details and characteristics of ground surfaces features, and from the other side is presenting new challenges in the data processing. Therefore, determination of elements of external orientation plays a substantial role the detail of Digital Terrain Models and artefact-free ortophoto generation. Parallel a research on the quality of acquired images from UAV and above the quality of products e.g. orthophotos are conducted. Despite so fast development UAV photogrammetry still exists the necessity of accomplishment Automatic Aerial Triangulation (AAT) on the basis of the observations GPS/INS and via ground control points. During low altitude photogrammetric flight, the approximate elements of external orientation registered by UAV are burdened with the influence of some shift/bias errors. In this article, methods of determination shift/bias error are presented. In the process of the digital aerial triangulation two solutions are applied. In the first method shift/bias error was determined together with the drift/bias error, elements of external orientation and coordinates of ground control points. In the second method shift/bias error was determined together with the elements of external orientation, coordinates of ground control points and drift/bias error equals 0. When two methods were compared the difference for shift/bias error is more than ±0.01 m for all terrain coordinates XYZ.

Reconstruction of measurable three-dimensional point cloud model based on large-scene archaeological excavation sites

This paper outlines a low-cost, user-friendly photogrammetric technique with nonmetric cameras to obtain excavation site digital sequence images, based on photogrammetry and computer vision. Digital camera calibration, automatic aerial triangulation, image feature extraction, image sequence matching, and dense digital differential rectification are used, combined with a certain number of global control points of the excavation site, to reconstruct the high precision of measured three-dimensional (3-D) models. Using the acrobatic figurines in the Qin Shi Huang mausoleum excavation as an example, our method solves the problems of little base-to-height ratio, high inclination, unstable altitudes, and significant ground elevation changes affecting image matching. Compared to 3-D laser scanning, the 3-D color point cloud obtained by this method can maintain the same visual result and has advantages of low project cost, simple data processing, and high accuracy. Structure-from-motion (SfM) is often used to reconstruct 3-D models of large scenes and has lower accuracy if it is a reconstructed 3-D model of a small scene at close range. Results indicate that this method quickly achieves 3-D reconstruction of large archaeological sites and produces heritage site distribution of orthophotos providing a scientific basis for accurate location of cultural relics, archaeological excavations, investigation, and site protection planning. This proposed method has a comprehensive application value.

An improved SIFT algorithm in the application of close-range Stereo image matching

As unmanned aerial vehicle (UAV) remote sensing is applied in small area aerial photogrammetry surveying, disaster monitoring and emergency command, 3D urban construction and other fields, the image processing of UAV has become a hot topic in current research. The precise matching of UAV image is a key problem, which affects the subsequent processing precision directly, such as 3D reconstruction and automatic aerial triangulation, etc. At present, SIFT (Scale Invariant Feature Transform) algorithm proposed by DAVID G. LOWE as the main method is, is widely used in image matching, since its strong stability to image rotation, shift, scaling, and the change of illumination conditions. It has been successfully applied in target recognition, SFM (Structure from Motion), and many other fields. SIFT algorithm needs the colour images to be converted into grayscale images, detects extremum points under different scales and uses neighbourhood pixels to generate descriptor. As we all know that UAV images with rich colour information, the SIFT algorithm improved through combining with the image colour information in this paper, the experiments are conducted from matching efficiency and accuracy compared with the original SIFT algorithm. The results show that the method which proposed in this paper decreases on the efficiency, but is improved on the precision and provides a basis choice for matching method.

ORTHO-RECTIFICATION OF NARROW BAND MULTI-SPECTRAL IMAGERY ASSISTED BY DSLR RGB IMAGERY ACQUIRED BY A FIXED-WING UAS

Abstract. Miniature Multiple Camera Array (MiniMCA-12) is a frame-based multilens/multispectral sensor composed of 12 lenses with narrow band filters. Due to its small size and light weight, it is suitable to mount on an Unmanned Aerial System (UAS) for acquiring high spectral, spatial and temporal resolution imagery used in various remote sensing applications. However, due to its wavelength range is only 10 nm that results in low image resolution and signal-to-noise ratio which are not suitable for image matching and digital surface model (DSM) generation. In the meantime, the spectral correlation among all 12 bands of MiniMCA images are low, it is difficult to perform tie-point matching and aerial triangulation at the same time. In this study, we thus propose the use of a DSLR camera to assist automatic aerial triangulation of MiniMCA-12 imagery and to produce higher spatial resolution DSM for MiniMCA12 ortho-image generation. Depending on the maximum payload weight of the used UAS, these two kinds of sensors could be collected at the same time or individually. In this study, we adopt a fixed-wing UAS to carry a Canon EOS 5D Mark2 DSLR camera and a MiniMCA-12 multi-spectral camera. For the purpose to perform automatic aerial triangulation between a DSLR camera and the MiniMCA-12, we choose one master band from MiniMCA-12 whose spectral range has overlap with the DSLR camera. However, all lenses of MiniMCA-12 have different perspective centers and viewing angles, the original 12 channels have significant band misregistration effect. Thus, the first issue encountered is to reduce the band misregistration effect. Due to all 12 MiniMCA lenses being frame-based, their spatial offsets are smaller than 15 cm and all images are almost 98% overlapped, we thus propose a modified projective transformation (MPT) method together with two systematic error correction procedures to register all 12 bands of imagery on the same image space. It means that those 12 bands of images acquired at the same exposure time will have same interior orientation parameters (IOPs) and exterior orientation parameters (EOPs) after band-to-band registration (BBR). Thus, in the aerial triangulation stage, the master band of MiniMCA-12 was treated as a reference channel to link with DSLR RGB images. It means, all reference images from the master band of MiniMCA-12 and all RGB images were triangulated at the same time with same coordinate system of ground control points (GCP). Due to the spatial resolution of RGB images is higher than the MiniMCA-12, the GCP can be marked on the RGB images only even they cannot be recognized on the MiniMCA images. Furthermore, a one meter gridded digital surface model (DSM) is created by the RGB images and applied to the MiniMCA imagery for ortho-rectification. Quantitative error analyses show that the proposed BBR scheme can achieve 0.33 pixels of average misregistration residuals length and the co-registration errors among 12 MiniMCA ortho-images and between MiniMCA and Canon RGB ortho-images are all less than 0.6 pixels. The experimental results demonstrate that the proposed method is robust, reliable and accurate for future remote sensing applications.

UAV photogrammetry for topographic monitoring of coastal areas

Coastal areas suffer degradation due to the action of the sea and other natural and human-induced causes. Topographical changes in beaches and sand dunes need to be assessed, both after severe events and on a regular basis, to build models that can predict the evolution of these natural environments. This is an important application for airborne LIDAR, and conventional photogrammetry is also being used for regular monitoring programs of sensitive coastal areas. This paper analyses the use of unmanned aerial vehicles (UAV) to map and monitor sand dunes and beaches. A very light plane (SwingletCam) equipped with a very cheap, non-metric camera was used to acquire images with ground resolutions better than 5cm. The Agisoft Photoscan software was used to orientate the images, extract point clouds, build a digital surface model and produce orthoimage mosaics. The processing, which includes automatic aerial triangulation with camera calibration and subsequent model generation, was mostly automated. To achieve the best positional accuracy for the whole process, signalised ground control points were surveyed with a differential GPS receiver. Two very sensitive test areas on the Portuguese northwest coast were analysed. Detailed DSMs were obtained with 10cm grid spacing and vertical accuracy (RMS) ranging from 3.5 to 5.0cm, which is very similar to the image ground resolution (3.2–4.5cm). Where possible to assess, the planimetric accuracy of the orthoimage mosaics was found to be subpixel. Within the regular coastal monitoring programme being carried out in the region, UAVs can replace many of the conventional flights, with considerable gains in the cost of the data acquisition and without any loss in the quality of topographic and aerial imagery data.

3D Building Modelling with Digital Map, Lidar Data and Video Image Sequences

AbstractThree‐dimensional (3D) reconstruction and texture mapping of buildings or other man‐made objects are key aspects for 3D city landscapes. An effective coarse‐to‐fine approach for 3D building model generation and texture mapping based on digital photogrammetric techniques is proposed. Three video image sequences, two oblique views of building walls and one vertical view of building roofs, acquired by a digital video camera mounted on a helicopter, are used as input images. Lidar data and a coarse two‐dimensional (2D) digital vector map used for car navigation are also used as information sources. Automatic aerial triangulation (AAT) suitable for a high overlap image sequence is used to give initial values of camera parameters of each image. To obtain accurate image lines, the correspondence between outlines of the building and their line features in the image sequences is determined with a coarse‐to‐fine strategy. A hybrid point/line bundle adjustment is used to ensure the stability and accuracy of reconstruction. Reconstructed buildings with fine textures superimposed on a digital elevation model (DEM) and ortho‐image are realistically visualised. Experimental results show that the proposed approach of 3D city model generation has a promising future in many applications.

Analysis of Epipolar Geometry in Linear Array Scanner Scenes

AbstractResampled imagery according to epipolar geometry, usually denoted as normalised imagery, is characterised by having conjugate points along the same row (or column). Such a characteristic makes normalised imagery an important prerequisite for many photogrammetric activities such as image matching, automatic aerial triangulation, automatic digital elevation model and orthophoto generation, and stereo viewing. The normalisation process requires having the input imagery in a digital format, which can be obtained by scanning analogue photographs or by direct use of digital cameras. To reduce the time gap between the data acquisition and product delivery, many small‐scale mapping projects now rely on digital cameras. Digital frame cameras still, in general, provide imagery with geometric resolution and ground coverage inferior to scanned images from analogue cameras. Linear array scanners (line cameras) have therefore emerged as a possible alternative to digital frame cameras, especially for high‐resolution space‐borne imaging, with performance comparable to that of analogue frame cameras.The normalisation process of frame images is a well‐established and straight forward procedure. On the other hand, the normalisation process of linear array scanner scenes is not as straightforward and is sometimes mysterious. For example, providers of space‐borne imagery furnish normalised line scanner imagery while the user community is not aware of the underlying process. This paper presents a comprehensive analysis of the epipolar geometry in linear array scanner scenes. Special emphasis is directed towards scanners moving with constant velocity and attitude since such a trajectory closely resembles the imaging geometry of the majority of current space‐borne scanners. The research presented highlights the factors affecting the shape of the resulting epipolar lines such as the stereo coverage configuration and the geometric specifications of the imaging system. In addition, the paper outlines a comparative analysis of the normalisation process for frame and line cameras. The presented concepts are verified through experimental results with synthetic data.

Practical Experiences with Automatic Aerial Triangulation: Editorial Response Based on Comments by G. Büyüksalih and the International Editorial Board of The Photogrammetric Record

Practical Experiences with Automatic Aerial Triangulation: Editorial Response Based on Comments by G. Büyüksalih and the International Editorial Board of <i>The Photogrammetric Record</i>

Practical Experiences with Automatic Aerial Triangulation: Paper by G. Büyüksalih and Z. Li

Practical Experiences with Automatic Aerial Triangulation: Paper by G. Büyüksalih and Z. Li

Practical experiences with automatic aerial triangulation using different software packages

AbstractDigital photogrammetry and softcopy workstations have added a new dimension to aerial triangulation, and some of the previously manual procedures are now available in automated form. Also, commercial software packages for automatic aerial triangulation came on to the market in the last decade. In this study, the results of automatic aerial triangulation using four triangulation software packages, Z/I Imaging's MATCH‐AT (ISMAT) and ISAT, LH Systems’ MST and VirtuoZo AAT, are presented. For the comparison of the results of the tie point extraction and the bundle block adjustment for triangulation, a sub‐block from the Uster block in Switzerland was selected and used. Differences between these software packages are discussed with respect to their functionality and performance in detail. The achieved standard deviation of image coordinates varies between 0·2 and 0·5 pixel for the systems tested. The more signalised control points and check points to be used, the better the σ0 estimate which could be expected. The average number of extracted tie points varied from system to system with respect to the matching method and the resolution of the images used in the programs. The digital aerial triangulation of the Uster sub‐block was faster than analytical triangulation and the elapsed time for triangulation of one image using different systems varies between about 0·5 and 6 min. Nevertheless, results also show that automated aerial triangulation is still prone to error in all systems. Image matching produces many blunders, which can only be handled efficiently by using a sophisticated blunder detection procedure, as is available with robust estimation. This is not the case for all the systems tested, however. There still remains a lot to be done in terms of development (improvement of matching, for example, by integrating robust estimation for error detection at an early stage, implementation of on‐line triangulation, better internal quality control procedures, more transparent and simple user interfaces and more useful information for the user with better software manuals). However, it was shown that automatic aerial triangulation provides higher redundancy and reliability of results and much more economical photogrammetric practice than ever before.

Towards automatic aerial triangulation

Digital photogrammetry has a great influence on aerial triangulation. Several digital aerial triangulation systems are now in various stages of development. Two distinct different approaches can be observed: interactive systems which require human operator guidance and softcopy workstations; and automatic systems. Automatic aerial triangulation systems attempt to reduce the aerial triangulation problem to a batch process, with little or no help of a human operator. Today's systems are close to meet this challenge, but the identification and measurement of control points remains an interactive task. The paper focuses on automatic aerial triangulation. Major effort is spent on identifying essential tasks that are independent of existing systems. The tasks, such as selecting suitable tie points, determining accurate approximations and matching multiple images, are derived from the objectives of digital aerial triangulation and by considering the potential of image processing and computer vision. The solution of these essential tasks brings a myriad of challenging problems. The concluding remarks comment on the differences between traditional and digital approaches and discuss the consequences.