bluuebunny/crossref_metadata_2025 huggingface dataset:
CLASSIFICATION ACCURACY INCREASE USING MULTISENSOR DATA FUSION
Authors: Makarau A. , Palubinskas G. , Reinartz P.
Published: September 2012

Abstract. The practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.) but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral data) restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR) and optical data is widely performed for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode) and multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), unsupervised clustering (data representation on a finite domain and dimensionality reduction), and data aggregation (Bayesian or neural network). This framework allows a relevant way of multisource data combination following consensus theory. The classification is not inﬂuenced by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion of single polarization TerraSAR-X, WorldView-2 (VNIR or full set), and Digital Surface Model (DSM) data allow for different types of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results of WorldView-2 multispectral data (8 spectral bands) is provided and the numerical evaluation of the method in comparison to other established methods illustrates the advantage in the classification accuracy for many classes such as buildings, low vegetation, sport objects, forest, roads, rail roads, etc.
3D BUILDINGS EXTRACTION FROM AERIAL IMAGES
Authors: Melnikova O. , Prandi F.
Published: September 2012

Abstract. This paper introduces a semi-automatic method for buildings extraction through multiple-view aerial image analysis. The advantage of the used semi-automatic approach is that it allows processing of each building individually finding the parameters of buildings features extraction more precisely for each area. On the early stage the presented technique uses an extraction of line segments that is done only inside of areas specified manually. The rooftop hypothesis is used further to determine a subset of quadrangles, which could form building roofs from a set of extracted lines and corners obtained on the previous stage. After collecting of all potential roof shapes in all images overlaps, the epipolar geometry is applied to find matching between images. This allows to make an accurate selection of building roofs removing false-positive ones and to identify their global 3D coordinates given camera internal parameters and coordinates. The last step of the image matching is based on geometrical constraints in contrast to traditional correlation. The correlation is applied only in some highly restricted areas in order to find coordinates more precisely, in such a way significantly reducing processing time of the algorithm. The algorithm has been tested on a set of Milan's aerial images and shows highly accurate results.
CITY STRUCTURE ANALYSIS ON QUICKBIRD IMAGERY BY MULTISCALE RADON TRANSFORMATION
Authors: Barsi A.
Published: September 2012

Abstract. The radon transformation is a proven image analysis technique in medical image processing, although there are some applications with satellite images. Among these applications maybe the most interesting one is based on the feature that this mathematical transformation is capable of detecting roads and road junctions. This option makes the transformation similar to the well-known Hough transformation, but radon has better performance parameters. Roads are ground objects having linear element borderlines, which can be extracted from radon analysis. Since roads and streets are crucial objects in urban environment analysis, they are excellent sources to get information about the city structure. The current paper introduces the short mathematical background behind the technique, and then a multiscale approach is described. A Quickbird image captured about Budapest was used to create a test data set with different scales, where the radon transform is applied. The different scale image layers show different details about the road structure, therefore about the city architectural structure. The result of the analysis can be presented in GIS database, thus the extracted information is able to be reused in further sophisticated applications.
FITTING OF PARAMETRIC BUILDING MODELS TO OBLIQUE AERIAL IMAGES
Authors: Panday U. S. , Gerke M.
Published: September 2012

Abstract. In literature and in photogrammetric workstations many approaches and systems to automatically reconstruct buildings from remote sensing data are described and available. Those building models are being used for instance in city modeling or in cadastre context. If a roof overhang is present, the building walls cannot be estimated correctly from nadir-view aerial images or airborne laser scanning (ALS) data. This leads to inconsistent building outlines, which has a negative inﬂuence on visual impression, but more seriously also represents a wrong legal boundary in the cadaster. Oblique aerial images as opposed to nadir-view images reveal greater detail, enabling to see different views of an object taken from different directions. Building walls are visible from oblique images directly and those images are used for automated roof overhang estimation in this research. A fitting algorithm is employed to find roof parameters of simple buildings. It uses a least squares algorithm to fit projected wire frames to their corresponding edge lines extracted from the images. Self-occlusion is detected based on intersection result of viewing ray and the planes formed by the building whereas occlusion from other objects is detected using an ALS point cloud. Overhang and ground height are obtained by sweeping vertical and horizontal planes respectively. Experimental results are verified with high resolution ortho-images, field survey, and ALS data. Planimetric accuracy of 1cm mean and 5cm standard deviation was obtained, while buildings' orientation were accurate to mean of 0.23° and standard deviation of 0.96° with ortho-image. Overhang parameters were aligned to approximately 10cm with field survey. The ground and roof heights were accurate to mean of – 9cm and 8cm with standard deviations of 16cm and 8cm with ALS respectively. The developed approach reconstructs 3D building models well in cases of sufficient texture. More images should be acquired for completeness of overhang results and automatic accuracy check of roof parameters.
COMBINATION OF TERRASAR-X AND OPTICAL IMAGERY FOR LU/LC MAPPING USING AN OBJECT-BASED APPROACH
Authors: Recio J. A. , Ruiz L. A. , Hermosilla T. , Herrera-Cruz V. , Fernandéz-Sarría A.
Published: September 2012

Abstract. In this study, the impact of the use of backscattering intensity and texture features obtained from TerraSAR-X images for LULC classification of agricultural and forest areas, and its combination with features extracted from Landsat 7 EMT+ optical imagery is analyzed. The performance of texture descriptors on radar images is evaluated. After data pre-processing and the definition of classes in the study area, every object is described by means of a set of features computed from the TerraSAR-X and optical imagery, using a plot-based approach. Cadastral cartographic limits are employed for objects definition. Next, objects are classified using decision trees combined with boosting techniques. The classification results are compared to the LULC contained in the testing database, and the errors evaluated in terms of the different groups of variables, the source of data used, and their performance for the variety of classes considered. The classification results bring some possibilities and limitations of combining features from optical and radar imagery, evidence the complementary information provided by both types of data to face these applications.
SYNERGISTIC USE OF OPTICAL AND RADAR DATA FOR RAPID MAPPING OF FOREST FIRES IN THE EUROPEAN MEDITERRANEAN
Authors: Bernhard E.-M. , Stein E. , Twele A. , Gähler M.
Published: September 2012

Abstract. In a classical approach, optical data are being used for forest fire detection in a rush mode. Difficulties arise due to persistent cloud coverage, haze layers and smoke plumes. In contrast, radar measurements offer high acquisition rates because of their ability to penetrate clouds and their independence of sun illumination. However, a visual interpretation of radar data is generally less intuitive than optical imagery for an untrained image analyst. Thus the main focus of our work was to combine the advantages of both data types and to develop a robust and fast but at the same time precise and transferable algorithm for burned area detection in the European Mediterranean region. Object-based change detection approaches and a synergistic use of optical and radar data can improve detection capabilities. The optical part of the algorithm covers very high resolution satellite images (like SPOT 5) including index calculation such as MSAVI, BAI and NDSWIR in single-temporal approaches and their temporal differences in multi- temporal approaches. Within the scope of both methodologies the burned area can be detected with an accuracy higher than 90%. In line with other authors (Libonati et al., 2011; Pereira et al., 1999) our work confirms the middle infrared band as crucial for burned area detection. The radar algorithm was based on TerraSAR-X StripMap data acquired before and after the forest fires. Different polarisations (VV and HH) have been used to improve the forest fire mapping capability. In addition, a comparison of burned and unburned areas was performed using different backscatter coefficients. These change detection techniques were based on image differences, image ratios and index calculation. The image segmentation was performed by using the new calculated layers. The burned area was then classified via a threshold given by the pre- and post- disaster differences. The classification result achieved an accuracy of 78%. This result shows the limitations of burned area mapping with microwaves. Therefore a combination of the optical and the radar technique, which takes advantage of both the optical accuracy and the ability of microwaves to penetrate clouds, led to the design and implementation of a single- and multi-temporal, object-based and semi-operational tool for burned area mapping.
DETECTION OF WATER SURFACES IN FULL-WAVEFORM LASER SCANNING DATA
Authors: Schmidt A. , Rottensteiner F. , Sörgel U.
Published: September 2012

Abstract. Airborne laser scanning has become a standard method for recording topographic data. A new generation of laser scanners digitises the complete waveform of the backscattered signal and thus offers the possibility of analysing the signal shape. As a product of the laser scanning, a digital surface model (DSM) or a digital terrain model (DTM) can be derived. In water regions, data acquisition by laser scanning is limited to the water surface because the near-infrared laser pulses hardly penetrate water. Therefore, a height model generated from laser scanner point clouds over water regions does not represent the actual terrain. The generation of a DTM thus requires the detection of water surfaces. In this study, a method for the detection and classification of water surfaces in airborne laser scanning data is proposed. The method works with both geometrical features (e.g. height or height variation) and characteristics of the pulses derived from the full waveform of the returned signal (e.g. intensity or pulse width). In our strategy, based on fuzzy logic, all classification parameters are derived automatically from training areas. According to their statistical distributions, the features are considered with individual weights. The aim of this paper is to analyse crucial features for classification and to investigate the potential of full waveform laser scanning data for this application. We present results from different areas with lakes and rivers, analysing the contribution of the individual groups of features for the detection of water surfaces.
KALMAN FILTER BASED FEATURE ANALYSIS FOR TRACKING PEOPLE FROM AIRBORNE IMAGES
Authors: Sirmacek B. , Reinartz P.
Published: September 2012

Abstract. Recently, analysis of man events in real-time using computer vision techniques became a very important research field. Especially, understanding motion of people can be helpful to prevent unpleasant conditions. Understanding behavioral dynamics of people can also help to estimate future states of underground passages, shopping center like public entrances, or streets. In order to bring an automated solution to this problem, we propose a novel approach using airborne image sequences. Although airborne image resolutions are not enough to see each person in detail, we can still notice a change of color components in the place where a person exists. Therefore, we propose a color feature detection based probabilistic framework in order to detect people automatically. Extracted local features behave as observations of the probability density function (pdf) of the people locations to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding pdf. First, we use estimated pdf to detect boundaries of dense crowds. After that, using background information of dense crowds and previously extracted local features, we detect other people in non-crowd regions automatically for each image in the sequence. We benefit from Kalman filtering to track motion of detected people. To test our algorithm, we use a stadium entrance image data set taken from airborne camera system. Our experimental results indicate possible usage of the algorithm in real-life man events. We believe that the proposed approach can also provide crucial information to police departments and crisis management teams to achieve more detailed observations of people in large open area events to prevent possible accidents or unpleasant conditions.
MAXIMUM LIKELIHOOD CLASSIFICATION OF HIGH-RESOLUTION SAR IMAGES IN URBAN AREA
Authors: Soheili Majd M. , Simonetto E. , Polidori L.
Published: September 2012

Abstract. In this work, we propose a state-of-the-art on statistical analysis of polarimetric synthetic aperture radar (SAR) data, through the modeling of several indices. We concentrate on eight ground classes which have been carried out from amplitudes, co-polarisation ratio, depolarization ratios, and other polarimetric descriptors. To study their different statistical behaviours, we consider Gauss, log- normal, Beta I, Weibull, Gamma, and Fisher statistical models and estimate their parameters using three methods: method of moments (MoM), maximum-likelihood (ML) methodology, and log-cumulants method (MoML). Then, we study the opportunity of introducing this information in an adapted supervised classification scheme based on Maximum–Likelihood and Fisher pdf. Our work relies on an image of a suburban area, acquired by the airborne RAMSES SAR sensor of ONERA. The results prove the potential of such data to discriminate urban surfaces and show the usefulness of adapting any classical classification algorithm however classification maps present a persistant class confusion between flat gravelled or concrete roofs and trees.
REGION BASED FOREST CHANGE DETECTION FROM CARTOSAT-1 STEREO IMAGERY
Authors: Tian J. , Leitloff J. , Krauß T. , Reinartz P.
Published: September 2012

Abstract. Tree height is a fundamental parameter for describing the forest situation and changes. The latest development of automatic Digital Surface Model (DSM) generation techniques allows new approaches of forest change detection from satellite stereo imagery. This paper shows how DSMs can support the change detection in forest area. A novel region based forest change detection method is proposed using single-channel CARTOSAT-1 stereo imagery. In the first step, DSMs from two dates are generated based on automatic matching technology. After co-registration and normalising by using LiDAR data, the mean-shift segmentation is applied to the original pan images, and the images of both dates are classified to forest and non-forest areas by analysing their histograms and height differences. In the second step, a rough forest change detection map is generated based on the comparison of the two forest map. Then the GLCM texture from the nDSM and the Cartosat-1 images of the resulting regions are analyzed and compared, the real changes are extracted by SVM based classification.
EVALUATION OF SELECTED FEATURES FOR CAR DETECTION IN AERIAL IMAGES
Authors: Tuermer S. , Leitloff J. , Reinartz P. , Stilla U.
Published: September 2012

Abstract. The extraction of vehicles from aerial images provides a wide area traffic situation within a short time. Applications for the gathered data are various and reach from smart routing in the case of congestions to usability validation of roads in the case of disasters. The challenge of the vehicle detection task is finding adequate features which are capable to separate cars from other objects; especially those that look similar. We present an experiment where selected features show their ability of car detection. Precisely, Haar-like and HoG features are utilized and passed to the AdaBoost algorithm for calculating the final detector. Afterwards the classifying power of the features is accurately analyzed and evaluated. The tests a carried out on aerial data from the inner city of Munich, Germany and include small inner city roads with rooftops close by which raise the complexity factor.
QUANTITATIVE ASSESSMENT OF DIGITAL TOPOGRAPHIC DATA FROM DIFFERENT SOURCES
Authors: Yastikli N. , Esirtgen F. , Sefercik U. G.
Published: September 2012

Abstract. This study includes the quantitative assessment of digital topographic data of the terrain from photogrammetric methods, satellite imaging and RADAR techniques in test side in Istanbul. The side area covers 10 × 10 km2 and includes five different land cover types. The 3 m sampled DEM, which was produced by 1/1000 scaled photogrammetric maps is used as reference and tested against the 94 Ground Control Points (GCP). The 5 m and 30 spacing DEMs derived from 1/5000 scaled photogrammetric maps are used as test data sets as well as 20 m spacing SPOT DEM and 80 m spacing ASTER DEM. Besides, 90 m spacing DEM generated from InSAR technique with SRTM mission was also used as test data. The quantitative assessment of all DEMs were performed and reported based on FEMA and NDEP specifications. The quantitative assessment was carried out for open terrain (%19), forest (%15), built-up areas (%32), scrub and bushes (%26), and rough terrain (%8). The accuracy assessment and quality analyses had been conducted for each terrain classes to reflect the quality with more suitable approach. The 3 m spacing reference DEM is tested against the 94 GCP and obtained Root Mean Square Error (RMSE) for Z was in the range of 0.85 m (open terrain) to 1.94 m (rough terrain) for different terrain classes and 1.45 m using all terrain classes. The more than 20 GCP was available for each terrain classes for quantitative assessment of DEMs. As a result of quantitative assessment, RMSE Z of tested DEMs from photogrammetric methods, satellite imaging and RADAR techniques were computed for each land cover types based on international standards. It is no surprise that, the DEM from photogrammetry were more accurate than the DEM from satellite imaging (SPOT, ASTER) and RADAR (SRTM). As expected, open terrains have better results than the other classes like forest, scrub, built-up and rough terrain for all DEMs. The accomplished results of the quantitative assessment demonstrated the importance of the data source, resolution, and production methods of DEMs. The terrain class is important factor and can not be disregarded in quantitative assessment of digital topographic data.
HIGH RESOLUTION DSM AND CLASSIFIED VOLUMETRIC GENERATION: AN OPERATIONAL APPROACH TO THE IMPROVEMENT OF GEOSPATIAL INTELLIGENCE
Authors: Boccardo P. , Gentili G.
Published: September 2012

Abstract. As mentioned by Bacastow and Bellafiore, Geospatial Intelligence (GEOINT) is a field of knowledge, a process, and a profession. As knowledge, it is information integrated in a coherent space-time context that supports descriptions, explanations, or forecasts of human activities with which decision makers take action. As a process, it is the means by which data and information are collected, manipulated, geospatially reasoned, and disseminated to decision-makers. The geospatial intelligence professional establishes the scope of activities, interdisciplinary associations, competencies, and standards in academe, government, and the private sectors. Taking into account the fact that GEOINT is crucial for broad organizations, BLOM Group, a leading International provider within acquisition, processing and modeling of geographic information and ITHACA, a non-profit organization devoted to products and services delivering to the UN System in the field of geomatics, set up and provided GEOINT data to the main Italian companies operating in the field of mobile phone networking. This data, extremely useful for telecom network planning, have derived and produced using a standardized and effective (from the production point of view) approach. In this paper, all the procedures used for the production are described and tested with the aim to investigate the suitability of the data and the procedures themselves to any others possible fields of application.
OBJECT BASED IMAGE ANALYSIS TO SUPPORT ENVIRONMENTAL MONITORING UNDER THE EUROPEAN HABITAT DIRECTIVE: A CASE STUDY FROM DECOVER
Authors: Buck O. , Peter B. , Völker A. , Donning A.
Published: September 2012

Abstract. DeCOVER serves as a national extension of the European Global Monitoring for Environment and Security (GMES) initiative. It was initiated to develop land cover information services adapted to German user needs. One of its three service developments pillars is the application of Remote Sensing to support environmental monitoring schemes under the European Habitats Directive.Within two DeCOVER test sites located in North-Rhine Westphalia/Germany an object-based indicator classification approach is currently being developed to monitor heath habitats of importance under the Habitats Directive. While many previous Remote Sensing projects have focused on the discrete classification of habitat types to replace fieldwork, our approach is embedded in a strong operational context to a) focus and direct fieldwork efforts by pre-field visit assessment of habitat changes (change detection) and b) support fieldwork by contributing quality parameters and GIS-ready geometries. Using Geoeye satellite data (VHR component) and RapidEye satellite images (Multi-temporal HR component) together with existing habitat and biotope maps (knowledge and post-classification component) an image analysis approach is realised using object-based classification routines based on data mining tools to derive training information. To extract meaningful objects of heath-, sand- and grassland from the VHR-data, training sample areas have to be assigned. Thresholds and appropriate features for describing these samples are analysed by statistical algorithms and are used in the following classification. A multi-temporal approach for the acquisition of tree habitat areas integrates two RapidEye scenes into the classification process. To validate classification accuracies and potential transects were sampled in the field and analyzed for their structural composition using top view field photos of 1m2. First results demonstrate the realistic option to directly support the fieldwork or reduce its post-processing costs.
ORTHORECTIFICATION OF WORLD VIEW 2 STEREO PAIR USING A NEW RIGOROUS ORIENTATION MODEL
Authors: Deltsidis P. , Ioannidis C.
Published: September 2012

Abstract. The use of very high resolution satellite images in photogrammetric works is increasing continuously, especially in works such as orthoimages production or Digital Elevation Models extraction. A large number of new photogrammetric uses of Earth Observation data appear due to improvement of the geometric accuracy of the products. The use of a rigorous orientation geometric model for the georeferencing of satellite image data increases the geometric potential of the photogrammetric procedure relating the satellite image to the ground point coordinates. The application of the commonly used geometric models demands the use of several control points (GCPs); the exact number of GCPs depends on the unknown parameters that need to be defined. In this paper a new rigorous orientation geometric model for pushbroom linear array sensor based on collinearity equations is described. The model uses the interior orientation parameters and orbital data; in its full version the model has 24 unknown parameters. The advantages of this model in comparison with the normal use of collinearity equations are the existence of less unknown parameters and the use of three observation equations for each observed point on the image. The scale factor in collinearity equations is not eliminated but it is modelled as a second order surface polynomial.
ASSESSMENT OF ULTRACAMD PERFORMANCE IN AN ARID ENVIRONMENT – CASE STUDY IN LIBYA
Authors: Gneeniss A. S. , Mills J. P. , Miller P. E.
Published: September 2012

Abstract. Large format digital aerial cameras are now in widespread commercial operation. Despite the advantages of the new cameras over their traditional film counterparts, systematic image errors have been observed in all existing large format digital cameras. Organizations such as the USGS and EuroSDR have therefore focused efforts on calibration, validation and certification of digital camera systems. However, to-date, few studies have assessed the effects of extreme temperature and humidity variations on these camera systems, and the potential for self-calibration in this respect. This research addresses these issues through investigation of the UltraCamD, with test data acquired over a range of climatic zones in Libya. This presentation will report the preliminary results of self-calibration using a bundle block adjustment for an UltraCamD system, based upon aerial data acquired for two test sites during a single field campaign. The datasets were flown at two different flying heights and incorporate differing block geometries. A SOCET SET (v5.4.1) digital photogrammetric workstation was used to triangulate the imagery with investigation of different tie point densities. Following this, a self-calibrating bundle block adjustment was performed using the BLUH software provided by the University of Hannover. Initial testing investigated the influence of tie point density and different coordinate systems and datums on the bundle adjustment results. Further testing was then performed within BLUH in order to determine the optimal set of additional parameters, compensating for systematic image errors and impact upon object space coordinates of independent check points and ground control points.
COLOR TEXTONS FOR BUILDING DETECTION
Authors: Hänsch R. , Hellwich O.
Published: September 2012

Abstract. Textons are known to be powerful operators in capturing textural properties of image regions. This paper proposes a new method to consistently combine structural cues as well as color information in an unified framework of color textons. They are used as features to detect buildings from optical imagery. Despite the simple classification algorithm, presented results are promising and show the usefulness of the proposed feature operator in remote sensing applications.
A HYBRID APPROACH TO EXTRACTION AND REFINEMENT OF BUILDING FOOTPRINTS FROM AIRBORNE LIDAR DATA
Authors: Huang H. , Sester M.
Published: September 2012

Abstract. This work presents a combined bottom-up and top-down approach to extraction and refinement of building footprints from airborne LIDAR data. Building footprints are interesting for many applications in urban planning. The cadastral maps, however, may be limited for certain areas or not be updated frequently. Airborne laser scanning data is therefore considered by many people in the last decade as an important alternative data for change detection and update of building footprints. Laser scanning data of city scenes, however, often shows noise and incompleteness because of, e.g., the clutter by vegetation and the reflection of windows/waterlogged depressions on the roof. Results of the bottom-up detection may thus be limited to incomplete or irregular polygons. We employ 3D Hough transform to detect the building points. An improved joint multiple-plane detection scheme is proposed to find and label the laser points on multiple roof facets synchronously. The bottom-up processing provides not only a rough point segmentation but also additional 3D information, e.g., roof heights and horizontal ridges. Using these as priors, a top-down reconstruction is conducted via generative models. We consider the building footprint as an assembly of regular primitives. A statistical search by means of Reversible Jump Markov Chain Monte Carlo and Maximum A Posteriori estimation is implemented to find the optimal configuration of the footprint. By these means a robust and plausible reconstruction is guaranteed. First results on point clouds with various resolutions show the potential of this approach.
A DTM MULTI-RESOLUTION COMPRESSED MODEL FOR EFFICIENT DATA STORAGE AND NETWORK TRANSFER
Authors: Biagi L. , Brovelli M. , Zamboni G.
Published: September 2012

Abstract. In recent years the technological evolution of terrestrial, aerial and satellite surveying, has considerably increased the measurement accuracy and, consequently, the quality of the derived information. At the same time, the smaller and smaller limitations on data storage devices, in terms of capacity and cost, has allowed the storage and the elaboration of a bigger number of instrumental observations. A significant example is the terrain height surveyed by LIDAR (LIght Detection And Ranging) technology where several height measurements for each square meter of land can be obtained. The availability of such a large quantity of observations is an essential requisite for an in-depth knowledge of the phenomena under study. But, at the same time, the most common Geographical Information Systems (GISs) show latency in visualizing and analyzing these kind of data. This problem becomes more evident in case of Internet GIS. These systems are based on the very frequent flow of geographical information over the internet and, for this reason, the band-width of the network and the size of the data to be transmitted are two fundamental factors to be considered in order to guarantee the actual usability of these technologies. In this paper we focus our attention on digital terrain models (DTM's) and we briefly analyse the problems about the definition of the minimal necessary information to store and transmit DTM's over network, with a fixed tolerance, starting from a huge number of observations. Then we propose an innovative compression approach for sparse observations by means of multi-resolution spline functions approximation. The method is able to provide metrical accuracy at least comparable to that provided by the most common deterministic interpolation algorithms (inverse distance weighting, local polynomial, radial basis functions). At the same time it dramatically reduces the number of information required for storing or for transmitting and rebuilding a digital terrain model dataset. A brief description of the method is presented and comparisons about the accuracy and data-store compression obtained with respect to other interpolators are shown.
MULTI-STAGE APPROACH TO TRAVEL-MODE SEGMENTATION AND CLASSIFICATION OF GPS TRACES
Authors: Zhang L. , Dalyot S. , Eggert D. , Sester M.
Published: September 2012

Abstract. This paper presents a multi-stage approach toward the robust classification of travel-modes from GPS traces. Due to the fact that GPS traces are often composed of more than one travel-mode, they are segmented to find sub-traces characterized as an individual travel-mode. This is conducted by finding individual movement segments by identifying stops. In the first stage of classification three main travel-mode classes are identified: pedestrian, bicycle, and motorized vehicles; this is achieved based on the identified segments using speed, acceleration and heading related parameters. Then, segments are linked up to form sub-traces of individual travel-mode. After the first stage is achieved, a breakdown classification of the motorized vehicles class is implemented based on sub-traces of individual travel-mode of cars, buses, trams and trains using Support Vector Machines (SVMs) method. This paper presents a qualitative classification of travel-modes, thus introducing new robust and precise capabilities for the problem at hand.
OPTIMIZING TERRESTRIAL LASER SCANNING MEASUREMENT SET-UP
Authors: Soudarissanane S. , Lindenbergh R.
Published: September 2012

Abstract. One of the main applications of the terrestrial laser scanner is the visualization, modeling and monitoring of man-made structures like buildings. Especially surveying applications require on one hand a quickly obtainable, high resolution point cloud but also need observations with a known and well described quality. To obtain a 3D point cloud, the scene is scanned from different positions around the considered object. The scanning geometry plays an important role in the quality of the resulting point cloud. The ideal set-up for scanning a surface of an object is to position the laser scanner in such a way that the laser beam is near perpendicular to the surface. Due to scanning conditions, such an ideal set-up is in practice not possible. The different incidence angles and ranges of the laser beam on the surface result in 3D points of varying quality. The stand-point of the scanner that gives the best accuracy is generally not known. Using an optimal stand-point of the laser scanner on a scene will improve the quality of individual point measurements and results in a more uniform registered point cloud. The design of an optimum measurement setup is deﬁned such that the optimum stand-points are identiﬁed to fulﬁll predeﬁned quality requirements and to ensure a complete spatial coverage. The additional incidence angle and range constraints on the visibility from a view point ensure that individual scans are not affected by bad scanning geometry effects. A complex and large room that would normally require ﬁve view point to be fully covered, would require nineteen view points to obtain full coverage under the range and incidence angle constraints.
THE ASSESSMENT OF THE ABSOLUTE PLANIMETRIC ACCURACY OF AIRBORNE LASERSCANNING
Authors: Höhle J.
Published: September 2012

Abstract. In acquisition and processing of Digital Elevation Models (DEMs) derived from airborne laser scanning (ALS) blunders and systematic errors occur. An assessment of the geometric quality of DEMs is necessary during production and before using the final DEM for an application. The vertical as well as the horizontal (planimetric) accuracy have to be assessed. Commonly agreed accuracy measures and procedures are necessary. The work of existing procedures and standards is analyzed. A new method to derive the absolute planimetric accuracy of ALS point clouds reliably and accurately is described. It is based on derivation of roof planes which are intersected to lines and points from the laser foot prints. The coordinates of generated points are then compared with points determined by aerial photogrammetry. From the differences accuracy measures are derived. Tests regarding the type of error distribution are carried out in order to apply either standard accuracy measures or robust accuracy measures. The results of a practical test are used to assess the planimetric accuracy by means of various accuracy measures. Suggestions for a new standard for the assessment of the absolute planimetric accuracy of airborne laserscanning are made.
THE FUSION OF INDIVIDUAL TREE DETECTION AND VISUAL INTERPRETATION IN ASSESSMENT OF FOREST VARIABLES FROM LASER POINT CLOUDS
Authors: Kankare V. , Vastaranta M. , Holopainen M. , Yu X. , Hyyppä J. , Hyyppä H.
Published: September 2012

Abstract. In this study we searched the obtainable accuracy of forest inventory based on the individual tree detection (ITD) by using fusion of automatic ITD (ITDauto) and visual interpretation of laser point clouds. Current ITD algorithms, mostly based on segmentation of canopy height models (CHMs), are not able to utilize the whole information included in three-dimensional point clouds. We hypothesized that visual interpretation of the point cloud could provide so-called "best case" tree detection that could be achievable automatically. We refer to this method consisting of ITDauto and visual interpretation as ITDvisual. We assessed the plot level accuracies of the ITDauto and ITDvisual in boreal managed forest conditions using 322 plots. Based on the results the accuracy of ITD can be improved with visual interpretation. Omission trees are mainly missing from both ITD-methods. ITDvisual produced more accurate estimates for all forest variables compared to ITDauto, e.g. RMSE% in volume decreased from 33.3% to 27.8% and bias% in volume from 4.1% to 2.3%. Area-based approach (ABA) is becoming more general for operational forest inventories with sparser laser data. ITDvisual would be justified if it could replace expensive field work in plot-wise measurements needed for ABA. Further research is needed in the use of ITD results as a reference for ABA.
RADIOMETRIC CALIBRATION OF TLS INTENSITY: APPLICATION TO SNOW COVER CHANGE DETECTION
Authors: Anttila K. , Kaasalainen S. , Krooks A. , Kaartinen H. , Kukko A. , Manninen T. , Lahtinen P. , Siljamo N.
Published: September 2012

Abstract. This paper reports on the radiometric calibration and the use of calibrated intensity data in applications related to snow cover monitoring with a terrestrial laser scanner (TLS). An application of the calibration method to seasonal snow cover change detection is investigated. The snow intensity from TLS data was studied in Sodankylä, Finland during the years 2008–2009 and in Kirkkonummi, Finland in the winter 2010–2011. The results were used to study the behaviour of TLS intensity data on different types of snow and measurement geometry. The results show that the snow type seems to have little or no effect on the incidence angle behaviour of the TLS intensity and that the laser backscatter from the snow surface is not directly related to any of the snow cover properties, but snow structure has a clear effect on TLS intensity.
GEOREFERENCING OF TLS DATA FOR INDUSTRIAL INDOOR COMPLEX SCENES: BEYOND CURRENT SOLUTIONS
Authors: Hullo J.-F. , Grussenmeyer P. , Landes T. , Thibault G.
Published: September 2012

Abstract. Current Terrestrial Laser Scanners (TLS) allow fast acquisitions of many dense point clouds. This technology is widely used within industrial complex scenes. The precise georeferencing of all the per-station point clouds is a crucial stage. Nowadays, the use of targets or tacheometry for precise georeferencing is time-consuming and then limits the number of surveys. The purpose of this article is to present and analyse a primitive based registration using points, lines and planes automatically extracted and paired in the station point clouds. Our tests highlight the advantages and the current limitations of this approach. We also discuss the recent, and probably future, improvements of indoor geolocation systems as possible ways of research for the georeferencing of TLS data into industrial complex scenes.
PERFORMANCE ANALYSIS OF A POLE AND TREE TRUNK DETECTION METHOD FOR MOBILE LASER SCANNING DATA
Authors: Lehtomäki M. , Jaakkola A. , Hyyppä J. , Kukko A. , Kaartinen H.
Published: September 2012

Abstract. Dense point clouds can be collected efficiently from large areas using mobile laser scanning (MLS) technology. Accurate MLS data can be used for detailed 3D modelling of the road surface and objects around it. The 3D models can be utilised, for example, in street planning and maintenance and noise modelling. Utility poles, traffic signs, and lamp posts can be considered an important part of road infrastructure. Poles and trees stand out from the environment and should be included in realistic 3D models. Detection of narrow vertical objects, such as poles and tree trunks, from MLS data was studied. MLS produces huge amounts of data and, therefore, processing methods should be as automatic as possible and for the methods to be practical, the algorithms should run in an acceptable time. The automatic pole detection method tested in this study is based on first finding point clusters that are good candidates for poles and then separating poles and tree trunks from other clusters using features calculated from the clusters and by applying a mask that acts as a model of a pole. The method achieved detection rates of 77.7% and 69.7% in the field tests while 81.0% and 86.5% of the detected targets were correct. Pole-like targets that were surrounded by other objects, such as tree trunks that were inside branches, were the most difficult to detect. Most of the false detections came from wall structures, which could be corrected in further processing.
VOXEL-BASED ESTIMATION OF PLANT AREA DENSITY FROM AIRBORNE LASER SCANNER DATA
Authors: Song Y. , Maki M. , Imanishi J. , Morimoto Y.
Published: September 2012

Abstract. Three-dimensional distribution of plant area density (PAD) was retrieved using airborne laser scanning (ALS) data. The calculation of PAD requires the number of laser-pulses intercepted by plant materials and which are not intercepted by trees (i.e., passed laser-pulses) for the spatial unit. To estimate the passed laser-pulses at a voxel (1-m voxel in this study), we traced every laser-return by using its flight line information. The assumption was that every laser-pulse traveled on the orthogonal line to flight line, meaning that the sensor mounted in the aircraft scanned perpendicularly to its flight line. Our function based on this assumption allowed PAD to be calculated. Consequently, we successfully obtained the PAD profiles at every 1-m voxel for the canopy area of 56 trees, which could be useful in the quantitative assessment of canopy structure at a broad scale.
AUTOMATIC EXTRACTION OF ROCK JOINTS FROM LASER SCANNED DATA BY MOVING LEAST SQUARES METHOD AND FUZZY K-MEANS CLUSTERING
Authors: Oh S. , Park H. D. , Jo Y. D.
Published: September 2012

Abstract. Recent development of laser scanning device increased the capability of representing rock outcrop in a very high resolution. Accurate 3D point cloud model with rock joint information can help geologist to estimate stability of rock slope on-site or off-site. An automatic plane extraction method was developed by computing normal directions and grouping them in similar direction. Point normal was calculated by moving least squares (MLS) method considering every point within a given distance to minimize error to the fitting plane. Normal directions were classified into a number of dominating clusters by fuzzy K-means clustering. Region growing approach was exploited to discriminate joints in a point cloud. Overall procedure was applied to point cloud with about 120,000 points, and successfully extracted joints with joint information. The extraction procedure was implemented to minimize number of input parameters and to construct plane information into the existing point cloud for less redundancy and high usability of the point cloud itself.
CALIBRATION OF FULL-WAVEFORM ALS DATA BASED ON ROBUST INCIDENCE ANGLE ESTIMATION
Authors: Abed F. M. , Mills J. P. , Miller P. E.
Published: September 2012

Abstract. Full-waveform airborne laser scanning data has shown its potential to enhance available segmentation and classification approaches through the additional information it can provide. However, this additional information is unable to directly provide a valid physical representation of surface features due to many variables affecting the backscattered energy during travel between the sensor and the target. Effectively, this delivers a mis-match between signals from overlapping flightlines. Therefore direct use of this information is not recommended without the adoption of a comprehensive radiometric calibration strategy that accounts for all these effects. This paper presents a practical and reliable radiometric calibration routine by accounting for all the variables affecting the backscattered energy, including the essential factor of angle of incidence. A new robust incidence angle estimation approach has been developed which has proven capable of delivering a reliable estimation for the scattering direction of the individual echoes. The routine was tested and validated both visually and statistically over various land cover types with simple and challenging surface trends. This proved the validity of this approach to deliver the optimal match between overlapping flightlines after calibration, particularly by adopting a parameter which accounts for the angle of incidence effect.
DETECTION OF COLLAPSED BUILDINGS BY CLASSIFYING SEGMENTED AIRBORNE LASER SCANNER DATA
Authors: Elberink S. O. , Shoko M. A. , Fathi S. A. , Rutzinger M.
Published: September 2012

Abstract. Rapid mapping of damaged regions and individual buildings is essential for efficient crisis management. Airborne laser scanner (ALS) data is potentially able to deliver accurate information on the 3D structures in a damaged region. In this paper we describe two different strategies how to process ALS point clouds in order to detect collapsed buildings automatically. Our aim is to detect collapsed buildings using post event data only. The first step in the workflow is the segmentation of the point cloud detecting planar regions. Next, various attributes are calculated for each segment. The detection of damaged buildings is based on the values of these attributes. Two different classification strategies have been applied in order to test whether the chosen strategy is capable of detect- ing collapsed buildings. The results of the classification are analysed and assessed for accuracy against a reference map in order to validate the quality of the rules derived. Classification results have been achieved with accuracy measures from 60–85% complete- ness and correctness. It is shown that not only the classification strategy influences the accuracy measures; also the validation meth- odology, including the type and accuracy of the reference data, plays a major role.