Accuracy Of Digital Surface Models Research Articles

Evaluation of DSMs generated from multi-temporal aerial photographs using emerging structure from motion–multi-view stereo technology

An accuracy assessment of digital surface models (DSMs) generated from archived aerial photographs using the structure from motion–multi-view stereo (SfM–MVS) technique was carried out. A four-step accuracy-assessment procedure was adopted using aerial photography from eight periods, as follows. Step 1: generate a DSM and orthophoto from digital aerial photographs taken in 2013 and ground control points (GCPs) measured by GNSS. Step 2: assess the accuracy of the DSM by comparison with altitude measured by leveling survey. Step 3: generate other historical DSMs and orthophotos from historical aerial photographs using GCPs extracted from the DSM of 2013. Step 4: assess the accuracy of all historical DSMs by comparing with the leveling survey. Then re-calculate the accuracy of historical DSMs by reducing the inherent error in the 2013 DSM. The DSM based on the aerial photographs taken in 2013 was generated with a resolution of 48.2cm. The residual height error of the GCPs was 15.4cm. Validation against the altitudes of 171 points revealed that this DSM has a height root-mean-square-error (RMSE) of 24.1cm and is 9.2cm lower than the leveling data on average. Even using US military photos with unconfirmed detailed specifications, the model can measure the altitude with an RMSE value of 121.5cm. It appears therefore that analysis by SfM–MVS can give comparable measurement accuracy to traditional aerial photogrammetry. The low cost and high accuracy obtained with archived aerial photographs are worthy of special mention.

Monitoring the Topography of a Dynamic Tidal Inlet Using UAV Imagery

Unmanned Aerial Vehicles (UAVs) are being increasingly used to monitor topographic changes in coastal areas. Compared to Light Detection And Ranging (LiDAR) data or Terrestrial Laser Scanning data, this solution is low-cost and easy to use, while allowing the production of a Digital Surface Model (DSM) with a similar accuracy. Three campaigns were carried out within a three-month period at a lagoon-inlet system (Bonne-Anse Bay, La Palmyre, France), with a flying wing (eBee) combined with a digital camera. Ground Control Points (GCPs), surveyed by the Global Navigation Satellite System (GNSS) and post-processed by differential correction, allowed georeferencing DSMs. Using a photogrammetry process (Structure From Motion algorithm), DSMs and orthomosaics were produced. The DSM accuracy was assessed against the ellipsoidal height of a GNSS profile and Independent Control Points (ICPs) and the root mean square discrepancies were about 10 and 17 cm, respectively. Compared to traditional topographic surveys, this solution allows the accurate representation of bedforms with a wavelength of the order of 1 m and a height of 0.1 m. Finally, changes identified between both main campaigns revealed erosion/accretion areas and the progradation of a sandspit. These results open new perspectives to validate detailed morphological predictions or to parameterize bottom friction in coastal numerical models.

Building Zone Regulation Compliance Using LIDAR Data: Real-Life Tests in İstanbul

Airborne Laser scanning systems with light detection and ranging (LIDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LIDAR range data. The LIDAR technique facilitates the rapid production of models which contain ground and above surface objects in 3D (Jaafar et al., 1999). Today automatic / semi-automatic generation of 3D digital building models and other city furniture from LIDAR data is a very active area of scientific research (Elaksher and Bethel, 2002) indicates that manual surface reconstruction is very costly and time consuming, and the development of automated algorithms is of great importance. Currently with this LIDAR data in place, it is possible to derive models and information (e.g. the heights of the buildings).This paper focuses on and explains our efforts on automatically checking Buildings’ Zoning Regulation Compliance by integrating geometric information derived from 3D LIDAR data and semantic information acquired from 2D Implementation Development Maps

Benchmarking stereo-derived DSMs to lidar by land cover and slope for resource development in northern Ontario, Canada

ABSTRACTTopographic and elevation data are essential in the development of supporting infrastructure around mining sites. The de facto standard for acquiring elevation data is through light detection and ranging (lidar). The high labour and monetary cost of acquiring lidar has fostered more cost-effective approaches for creating elevation models that use stereo photogrammetry. To assess the accuracy of stereo-photogrammetry-derived elevation models and their potential application, we benchmark satellite (Worldview-2) and aircraft (South Central Ontario Orthoimagery Project; SCOOP) stereo-derived digital surface models (DSMs) against a lidar-derived DSM. Our results show that both stereo-derived DSMs have strong monotonic correlations with lidar across a range of land-cover types and slopes. The overall vertical accuracy of Worldview-2 and SCOOP DSMs are similar and do not meet the United States National Digital Elevation Program (NDEP) standards. However, accuracy assessment across land-cover types and slope categories show that specific land cover types (i.e. grass, row crops/pasture, sparse vegetation and marsh) on gently sloping terrain compare well to lidar data and meet NDEP accuracy standards. We situate the presented research in the context of northern resource development and discuss opportunities to improve the vertical accuracy of stereo-derived DSMs, for example, through unmanned aerial systems.

UAV Photogrammetry for Monitoring Changes in River Topography and Vegetation

This study aims to evaluate the accuracy of digital surface model (DSM) of river-channel morphology, which is derived from the imagery acquired with a low-cost digital camera on board an unmanned multi-copter. UAV photogrammetry at flight altitude of 100 m has been carried out before and after a man-made flood in the Jyoge River in Hiroshima Prefecture, Japan along with ground survey using RTK-GPS and Total Station. The UAV photogrammetry has demonstrated that the DSM reproduces the ground elevation very well with the maximum error of 4cm over a floodway where the vegetation height and density are low and that the DSM reasonably captures the thick vegetation cover over sandbars. It is also confirmed that the difference in DSM before and after the flood is due to the plant toppling over sandbars.

UAVを利用した空中写真の河川地形計測への適用性

Unmanned Aerial Vehicles (UAVs) are a new type of platform for high-resolution aerial photogrammetry. Despite of their growing applications there has been little assessment of their performance. This study reports the results from two field experiments designed to evaluate the accuracy of digital surface model (DSM) derived from the imagery acquired with a low-cost digital camera onboard an UAV. Concurrently the survey was carried out using RTK-GPS and Total Station. The first experiment was performed to evaluate the highest measurement precision at a parking lot at Hiroshima University. Another experiment was conducted in the reaches of the Jyoge River. Comparisons between the DSM and the survey data have pointed out that the number of Ground Control Point and the quality of photographic lens are important factors for high precision measurement and that the r.m.s. errors at altitude of 100 m are less than 2 cm, suggesting that UAV photogrammetry has high applicability to river morphological mapping.

Precision Assessment of UAVRS Based DSM in Disaster Emergency

Ground control point (GCP) is important for georeferencing remotely sensed images and topographic model. However, considering that GCP collection is sometimes a difficult, time-consuming and expensive task with high resolution (HR) data in remote and harsh environments, today unmanned aerial vehicle based remote sensing (UAVRS) is frequently used in geological disaster emergency monitoring and rescuing for its great advantage in collecting timely onsite images. In this paper, for evaluating the feasibility of the UAVRS in disaster emergency and high cut slope safety monitoring, the digital surface model (DSM) without GCPs based on Structure from Motion (SfM) is accessed, and results showed that the geometric accuracy of DSM was smaller than 1 percent, which prove the usefulness of DSM based on UAVRS in emergency. Comparing to normal disaster emergency, the method without GCPs can be more efficient and save the disaster emergency time by neglecting GCPs measurement.

GIS-Based Estimation of Potential Solar Energy on Flat Roofs in Maadi, Cairo, using True Ortho World View Image and Digital Surface Model

This research is concerned with the use of remotely sensed data in the estimation of the solar energy potentiality of the flat roofs in El Maadi District, Cairo. A solar radiation model was applied using a digital surface model (DSM) generated from two Worldview stereo satellite images using the digital photogrammetric workstation (Leica Photogrammetric Suite) LPS. The procedures included applying orientation with Rational Polynomial Coefficients (RPC) with and without GCPs, tie point measurements, aerial triangulation, automatic DSM and editing, true ortho-rectification. The accuracy of the DSM has been assessed and reported for both methods. The ortho-rectification, of stereo satellite images has been performed. A PAN true ortho-images with 0.5 m resolution resulted from applying orientation with rational polynomial coefficients (RPCs) refined with four accurate differential ground control points (DGCPs) and a high accurate DSM derived from stereo images were generated. Two classification methods were conducted (SVM and ANN) on the true ortho-image. First method without utilizing texture, the second utilizing texture features of Grey Level Co-occurrence Matrix (GLCM) as inputs. The SVM of the ortho-image and texture features overall accuracy was 93.4%, kappa coefficient was 0.92 and ANN of ortho-image and texture features overall accuracy of 91.2% and kappa coefficient was 0.90. Using the generated DSM as input, the area solar radiation model in ESRI ArcGIS was run. The model resulted in an annual total radiation grid for year 2015 which was converted to average daily radiation in kilowatt hour/square meters per day. A sample of 265 buildings footprints was extracted from the classification and used for the estimation. Assuming that 50% roof areas are available for solar Photovoltaic (PV), 12% PV grid conversion efficiency and 0.6 performance ratio and using ESRI zonal statistics tool, the technical potentiality for solar PV electricity generation for the sample buildings was calculated, classified into five classes and mapped.

UAV 영상을 활용한 수변구조물의 DSM 생성 및 정확도 분석

If the damage analysis on river-side facilities such as dam, river bank structures and bridges caused by disasters such as typhoon, flood, etc. becomes available, it can be a great help for disaster recovery and decision-making. In this research, We tried to extract a Digital Surface Model (DSM) and analyze the accuracy from Unmanned Air Vehicle (UAV) images on river-side facilities. We tried to apply stereo image-based matching technique, then extracted match results were united with one mosaic DSM. The accuracy was verified compared with a DSM derived from LIDAR data. Overall accuracy was around 3m of absolute and root mean square error. As an analysis result, we confirmed that exterior orientation parameters exerted an influence to DSM accuracy. For more accurate DSM generation, accurate EO parameters are necessary and effective interpolation and post process technique needs to be developed. And the damage analysis simulation with DSM has to be performed in the future.

IMPROVING THE ACCURACY OF ESTIMATED 3D POSITIONS USING MULTI-TEMPORAL ALOS/PRISM TRIPLET IMAGES

Abstract. In this paper, we present a method to improve the accuracy of a digital surface model (DSM) by utilizing multi-temporal triplet images. The Advanced Land Observing Satellite (ALOS) / Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) measures triplet images in the forward, nadir, and backward view directions, and a DSM is generated from the obtained set of triplet images. To generate a certain period of DSM, multiple DSMs generated from individual triplet images are compared, and outliers are removed. Our proposed method uses a traditional surveying approach to increase observations and solves multiple observation equations from all triplet images via the bias-corrected rational polynomial coefficient (RPC) model. Experimental results from using five sets of PRISM triplet images taken of the area around Saitama, north of Tokyo, Japan, showed that the average planimetric and height errors in the coordinates estimated from multi-temporal triplet images were 3.26 m and 2.71 m, respectively, and that they were smaller than those generated by using each set of triplet images individually. As a result, we conclude that the proposed method is effective for stably generating accurate DSMs from multi-temporal triplet images.

PHOTOGRAMMETRIC PROCESSING USING ZY-3 SATELLITE IMAGERY

Abstract. This paper evaluates the stereoscopic capacities of the Chinese sensor ZiYuan-3 (ZY-3) for the generation of photogrammetric products. The satellite was launched on January 9, 2012 and carries three high-resolution panchromatic cameras viewing in forward (22º), nadir (0º) and backward direction (-22º) and an infrared multi-spectral scanner (IRMSS), which is slightly looking forward (6º). The ground sampling distance (GSD) is 2.1m for the nadir image, 3.5m for the two oblique stereo images and 5.8m for the multispectral image. The evaluated ZY-3 imagery consists of a full set of threefold-stereo and a multi-spectral image covering an area of ca. 50km x 50km north-west of Barcelona, Spain. The complete photogrammetric processing chain was executed including image orientation, the generation of a digital surface model (DSM), radiometric image correction, pansharpening, orthoimage generation and digital stereo plotting. All 4 images are oriented by estimating affine transformation parameters between observed and nominal RPC (rational polynomial coefficients) image positions of 17 ground control points (GCP) and a subsequent calculation of refined RPC. From 10 independent check points RMS errors of 2.2m, 2.0m and 2.7m in X, Y and H are obtained. Subsequently, a DSM of 5m grid spacing is generated fully automatically. A comparison with the Lidar data results in an overall DSM accuracy of approximately 3m. In moderate and flat terrain higher accuracies in the order of 2.5m and better are achieved. In a next step orthoimages from the high resolution nadir image and the multispectral image are generated using the refined RPC geometry and the DSM. After radiometric corrections a fused high resolution colour orthoimage with 2.1m pixel size is created using an adaptive HSL method. The pansharpen process is performed after the individual geocorrection due to the different viewing angles between the two images. In a detailed analysis of the colour orthoimage artifacts are detected covering an area of 4691ha, corresponding to less than 2% of the imaged area. Most of the artifacts are caused by clouds (4614ha). A minor part (77ha) is affected by colour patch, stripping or blooming effects. For the final qualitative analysis on the usability of the ZY-3 imagery for stereo plotting purposes stereo combinations of the nadir and an oblique image are discarded, mainly due to the different pixel size, which produces difficulties in the stereoscopic vision and poor accuracy in positioning and measuring. With the two oblique images a level of detail equivalent to 1:25.000 scale is achieved for transport network, hydrography, vegetation and elements to model the terrain as break lines. For settlement, including buildings and other constructions a lower level of detail is achieved equivalent to 1:50.000 scale.

Assessing the quality of DSM from ALOS optical and radar data for automatic drainage extraction

The hydrological network analysis was based for many years on topographic maps or on aerial photos. The evolution of GIS software and the existence of plenty of satellite sensors collecting data with high spatial resolution made possible the creation of quite accurate Digital Surface Models (DSM) and the automatic drainage extraction. In this study the suitability and the accuracy of drainage network derived from ALOS optical and radar data are validated with reference to the respective information from the topographic maps of 1/50.000. DSM from ALOS PALSAR and ALOS PRISM are created, evaluated for both vertical and horizontal accuracy and used for automatic drainage extraction. A fifth order basin of Alfios River in Western Peloponese, Greece was selected for the validation. Hydrological parameters such as the stream length, the drainage frequency and the drainage density were used for the evaluation of seven DSMs. It was proved that the DSMs of the ALOS optical data are more suitable for hydrological analysis and that the vertical high accuracy of the DSM doesn’t guarantee its suitability for automatic drainage extraction.

DSM Accuracy Evaluation for the ISPRS Commission I Image Matching Benchmark

Abstract. To improve the quality of algorithms for automatic generation of Digital Surface Models (DSM) from optical stereo data in the remote sensing community, the Working Group 4 of Commission I: Geometric and Radiometric Modeling of Optical Airborne and Spaceborne Sensors provides on its website http://www2.isprs.org/commissions/comm1/wg4/benchmark-test.html a benchmark dataset for measuring and comparing the accuracy of dense stereo algorithms. The data provided consists of several optical spaceborne stereo images together with ground truth data produced by aerial laser scanning. In this paper we present our latest work on this benchmark, based upon previous work. As a first point, we noticed that providing the abovementioned test data as geo-referenced satellite images together with their corresponding RPC camera model seems too high a burden for being used widely by other researchers, as a considerable effort still has to be made to integrate the test datas camera model into the researchers local stereo reconstruction framework. To bypass this problem, we now also provide additional rectified input images, which enable stereo algorithms to work out of the box without the need for implementing special camera models. Care was taken to minimize the errors resulting from the rectification transformation and the involved image resampling. We further improved the robustness of the evaluation method against errors in the orientation of the satellite images (with respect to the LiDAR ground truth). To this end we implemented a point cloud alignment of the DSM and the LiDAR reference points using an Iterative Closest Point (ICP) algorithm and an estimation of the best fitting transformation. This way, we concentrate on the errors from the stereo reconstruction and make sure that the result is not biased by errors in the absolute orientation of the satellite images. The evaluation of the stereo algorithms is done by triangulating the resulting (filled) DSMs and computing for each LiDAR point the nearest Euclidean distance to the DSM surface. We implemented an adaptive triangulation method minimizing the second order derivative of the surface in a local neighborhood, which captures the real surface more accurate than a fixed triangulation. As a further advantage, using our point-to-surface evaluation, we are also able to evaluate non-uniformly sampled DSMs or triangulated 3D models in general. The latter is for example needed when evaluating building extraction and data reduction algorithms. As practical example we compare results from three different matching methods applied to the data available within the benchmark data sets. These results are analyzed using the above mentioned methodology and show advantages and disadvantages of the different methods, also depending on the land cover classes.

Generation of 2D Land Cover Maps for Urban Areas Using Decision Tree Classification

Abstract. A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objects like buildings, roads, grassland, trees, hedges, and walls from such an "intelligent" point cloud. The decision tree is derived from training areas which borders are digitized on top of a false-colour orthoimage. The produced 2D land cover map with six classes is then subsequently refined by using image analysis techniques. The proposed methodology is described step by step. The classification, assessment, and refinement is carried out by the open source software "R"; the generation of the dense and accurate digital surface model by the "Match-T DSM" program of the Trimble Company. A practical example of a 2D land cover map generation is carried out. Images of a multispectral medium-format aerial camera covering an urban area in Switzerland are used. The assessment of the produced land cover map is based on class-wise stratified sampling where reference values of samples are determined by means of stereo-observations of false-colour stereopairs. The stratified statistical assessment of the produced land cover map with six classes and based on 91 points per class reveals a high thematic accuracy for classes "building" (99 %, 95 % CI: 95 %-100 %) and "road and parking lot" (90 %, 95 % CI: 83 %-95 %). Some other accuracy measures (overall accuracy, kappa value) and their 95 % confidence intervals are derived as well. The proposed methodology has a high potential for automation and fast processing and may be applied to other scenes and sensors.

Raster Vs. Point Cloud LiDAR Data Classification

Abstract. Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surface. These effects may lead to uneven distribution of point cloud or some gaps that may affect the classification process. Researchers have investigated the conversion of LiDAR range point data to raster image for terrain modelling. Interpolation techniques have been used to achieve the best representation of surfaces, and to fill the gaps between the LiDAR footprints. Interpolation methods are also investigated to generate LiDAR range and intensity image data for land cover classification applications. In this paper, different approach has been followed to classifying the LiDAR data (range and intensity) for land cover mapping. The methodology relies on the classification of the point cloud data based on their range and intensity and then converted the classified points into raster image. The gaps in the data are filled based on the classes of the nearest neighbour. Land cover maps are produced using two approaches using: (a) the conventional raster image data based on point interpolation; and (b) the proposed point data classification. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to compare the results of the two approaches. Five different land cover classes can be distinguished in that area: buildings, roads and parking areas, trees, low vegetation (grass), and bare soil. The results show that an improvement of around 10 % in the classification results can be achieved by using the proposed approach.

Impact of Orthorectification on Simulated Compact Polarimetric RCM Data With Accurate Lidar DSM

Orthorectification using digital terrain models is a key issue for polarimetric complex synthetic aperture radar (SAR) data because resampling the complex data can corrupt the polarimetric phase, mainly in terrain with relief. Orthorectification will be also a sensitive issue with the compact polarimetric data of the future Canadian Radar Constellation Mission (RCM) sensor to be launched in 2018. Two orthorectification methods for the complex SAR data are thus proposed and compared: performing polarimetric processing in the image space before the geometric processing or in the ground space after the geometric processing. RCM compact polarimetric data using the requirements of the very high resolution (VHR) mode were simulated at the Canada Centre for Mapping and Earth Observation from fine-quad Radarsat-2 data acquired with different look angles over a hilly relief study site. Quantitative evaluations between the two methods, using a basis-invariant parameter (the entropy), were thus performed to evaluate the impact of orthorectification on the simulated VHR RCM data. To avoid the propagation of elevation errors into the final error budget, an accurate lidar digital surface model was used in the orthorectification. The results demonstrated that the oversampling and the noise floor that is used to generate the simulated VHR RCM data are the main factors, which corrupted the simulated VHR RCM data during its orthorectification with the ground-space method.

Can 3D Point Clouds Replace GCPs?

Abstract. Over the past decade, large-scale photogrammetric products have been extensively used for the geometric documentation of cultural heritage monuments, as they combine metric information with the qualities of an image document. Additionally, the rising technology of terrestrial laser scanning has enabled the easier and faster production of accurate digital surface models (DSM), which have in turn contributed to the documentation of heavily textured monuments. However, due to the required accuracy of control points, the photogrammetric methods are always applied in combination with surveying measurements and hence are dependent on them. Along this line of thought, this paper explores the possibility of limiting the surveying measurements and the field work necessary for the production of large-scale photogrammetric products and proposes an alternative method on the basis of which the necessary control points instead of being measured with surveying procedures are chosen from a dense and accurate point cloud. Using this point cloud also as a surface model, the only field work necessary is the scanning of the object and image acquisition, which need not be subject to strict planning. To evaluate the proposed method an algorithm and the complementary interface were produced that allow the parallel manipulation of 3D point clouds and images and through which single image procedures take place. The paper concludes by presenting the results of a case study in the ancient temple of Hephaestus in Athens and by providing a set of guidelines for implementing effectively the method.

Geometric Errors of Remote Sensing Images Over Forest and Their Propagation to Bidirectional Studies

This study focused on the need of accurate digital surface models rather than existing digital terrain models for the geometric correction of high spatial resolution images over forests. Based on both theoretical and experimental results, it was shown here that even for close to nadir observations (view angles less than 7°), the geometric error increased from within to beyond the pixel level when not taking into account the canopy height. This is particularly relevant for forest studies on bidirectional effects, data fusion and change detection techniques. The propagation of geometric errors for studies on bidirectional effects was quantified as a case study here, showing that geometric errors can easily mask such effects.

ACCURACY COMPARISON OF DIGITAL SURFACE MODELS CREATED BY UNMANNED AERIAL SYSTEMS IMAGERY AND TERRESTRIAL LASER SCANNER

Abstract. The main focus of the paper is a comparative study in which we have investigated, whether automatically generated digital surface models (DSM) obtained from unmanned aerial systems (UAS) imagery are comparable with DSM obtained from terrestrial laser scanning (TLS). The research is conducted at a pilot dike for coastal engineering. The effort and the achievable accuracy of both DSMs are compared. The error budgets of these two methods are investigated and the models obtained in each case compared against each other.

Automated bare earth extraction technique for complex topography in light detection and ranging surveys

Bare earth extraction is an important component to light detection and ranging (LiDAR) data analysis in terms of terrain classification. The challenge in providing accurate digital surface models is augmented when there is diverse topography within the data set or complex combinations of vegetation and built structures. Few existing algorithms can handle substantial terrain diversity without significant editing or user interaction. This effort presents a newly developed methodology that provides a flexible, adaptable tool capable of integrating multiple LiDAR data attributes for an accurate terrain assessment. The terrain extraction and segmentation (TEXAS) approach uses a third-order spatial derivative for each point in the digital surface model to determine the curvature of the terrain rather than rely solely on the slope. The utilization of the curvature has shown to successfully preserve ground points in areas of steep terrain as they typically exhibit low curvature. Within the framework of TEXAS, the contiguous sets of points with low curvatures are grouped into regions using an edge-based segmentation method. The process does not require any user inputs and is completely data driven. This technique was tested on a variety of existing LiDAR surveys, each with varying levels of topographic complexity.