bluuebunny/crossref_metadata_2025 huggingface dataset:
OBJECT-BASED CHANGE DETECTION USING GEOREFERENCED UAV IMAGES
Authors: Shi J. , Wang J. , Xu Y.
Published: September 2012

Abstract. Unmanned aerial vehicles (UAV) have been widely used to capture and down-link real-time videos/images. However, their role as a low-cost airborne platform for capturing high-resolution, geo-referenced still imagery has not been fully utilized. The images obtained from UAV are advantageous over remote sensing images as they can be obtained at a low cost and potentially no risk to human life. However, these images are distorted due to the noise generated by the rotary wings which limits the usefulness of such images. One potential application of such images is to detect changes between the images of the same area which are collected over time. Change detection is of widespread interest due to a large number of applications, including surveillance and civil infrastructure. Although UAVs can provide images with high resolution in a portable and easy way, such images only cover small parts of the entire field of interest and are often with high deformation. Until now, there is not much application of change detection for UAV images. Also the traditional pixel-based change detection method does not give satisfactory results for such images. In this paper, we have proposed a novel object-based method for change detection using UAV images which can overcome the effect of deformation and can fully utilize the high resolution capability of UAV images. The developed method can be divided into five main blocks: pre-processing, image matching, image segmentation and feature extraction, change detection and accuracy evaluation. The pre-processing step is further divided into two sub-steps: the first sub-step is to geometrically correct the bi-temporal image based on the geo-reference information (GPS/INS) installed on the UAV system, and the second sub-step is the radiometric normalization using a histogram method. The image matching block uses the well-known scale-invariant feature transform (SIFT) algorithm to match the same areas in the images and then resample them. The image segmentation and feature extraction block is used to separate the images to different meaningful regions by the mean shift method, extract the textural features and contextual features (polygon,etc.) , Based on the features extracted above as well as the SIFT features of the area, the optimization result is achieved by considering the neighbourhood information .The proposed method is being tested by using multi-temporal images acquired by UAV. The results confirm the effectiveness of the proposed approach.
PHOTOGRAMMETRIC RECORDING OF ANCIENT BUILDINGS BY USING UNMANNED HELICOPTERS – CASES IN CHINA
Authors: Li Z. , Li Y.
Published: September 2012

Abstract. The survey of ancient buildings is complex work. Chinese wooden buildings, with their complex structure and exquisite decorations, pose a great challenge for survey work. Therefore, it is necessary to introduce unmanned-helicopter photogrammetry and laser scanning technology to facilitate the work. This paper uses three cases to explain the application of these new technologies in China's architectural heritage protection and analyzes the special features and main operational techniques of the survey wor k.
MANNED OR UNMANNED – DOES THIS REALLY MATTER?
Authors: Neininger B. , Hacker J. M.
Published: September 2012

Abstract. This paper is an attempt to compare, and possibly combine, the capabilities and technologies available for using either small UAS or small manned aircraft, or both, for environmental research applications including geomatics. The paper is emphasising the view that instead of making one or the other platform technology (manned or unmanned) the deciding factor for specific applications in an a priori sense, it would be a better approach to evaluate each technology's suitability and merits in terms of ease of use (instrumentation integration, operational aspects, potential restrictions, safety, etc.) and also cost-efficiency. As will be shown, in some cases, this might even mean that a combination of manned and unmanned aerial platforms could be the optimum choice for a specific set of tasks. The paper introduces a number of manned and unmanned small aerial platforms and looks at their specific proven and envisaged capabilities for specific tasks. It also introduces the concept of using manned and unmanned aerial platform in tandem, maximising the usefulness of both technologies together for specific tasks. The authors' intent is to encourage a close look at all technologies available today, or in the near future, and to make that the basis for decisions about which ones are the most suitable ones for specific applications or projects. Two field campaigns in which METAIR and ARA have operated their small manned aerial platforms are re-analysed to give an example of the considerations that should be evaluated to decide which platform technology might be the most suitable one for a specific project. One of the projects ("TIPPEX") was flown in 2008 in Northern Australia, while the other one ("MAIOLICA") had flight campaigns in 2009 and 2011 in Switzerland.
MOBILE STEREO-MAPPER: A PORTABLE KIT FOR UNMANNED AERIAL VEHICLES
Authors: Li-Chee-Ming J. , Armenakis C. , Lee R.
Published: September 2012

Abstract. A low-cost portable light-weight mobile stereo-mapping system (MSMS) is under development in the GeoICT Lab, Geomatics Engineering program at York University. The MSMS is designed for remote operation on board unmanned aerial vehicles (UAV) for navigation and rapid collection of 3D spatial data. Pose estimation of the camera sensors is based on single frequency RTK-GPS, loosely coupled in a Kalman filter with MEMS-based IMU. The attitude and heading reference system (AHRS) calculates orientation from the gyro data, aided by accelerometer and magnetometer data to compensate for gyro drift. Two low-cost consumer digital cameras are calibrated and time-synchronized with the GPS/IMU to provide direct georeferenced stereo vision, while a video camera is used for navigation. Object coordinates are determined using rigorous photogrammetric solutions supported by direct georefencing algorithms for accurate pose estimation of the camera sensors. Before the MSMS is considered operational its sensor components and the integrated system itself has to undergo a rigorous calibration process to determine systematic errors and biases and to determine the relative geometry of the sensors. In this paper, the methods and results for system calibration, including camera, boresight and leverarm calibrations are presented. An overall accuracy assessment of the calibrated system is given using a 3D test field.
UAV PHOTOGRAMMETRY FOR MAPPING AND 3D MODELING – CURRENT STATUS AND FUTURE PERSPECTIVES
Authors: Remondino F. , Barazzetti L. , Nex F. , Scaioni M. , Sarazzi D.
Published: September 2012

Abstract. UAV platforms are nowadays a valuable source of data for inspection, surveillance, mapping and 3D modeling issues. New applications in the short- and close-range domain are introduced, being the UAVs a low-cost alternatives to the classical manned aerial photogrammetry. Rotary or fixed wing UAVs, capable of performing the photogrammetric data acquisition with amateur or SLR digital cameras, can fly in manual, semi-automated and autonomous modes. With a typical photogrammetric pipeline, 3D results like DSM/DTM, contour lines, textured 3D models, vector data, etc. can be produced, in a reasonable automated way. The paper reports the latest developments of UAV image processing methods for photogrammetric applications, mapping and 3D modeling issues. Automation is nowadays necessary and feasible at the image orientation, DSM generation and orthophoto production stages, while accurate feature extraction is still an interactive procedure. New perspectives are also addressed.
UAV-MAPPING – A USER REPORT
Authors: Mayr W.
Published: September 2012

Abstract. This paper reports on first hand experiences in operating an unmanned airborne system (UAS) for mapping purposes in the environment of a mapping company. Recently, a multitude of activities in UAVs is visible, and there is growing interest in the commercial, industrial, and academic mapping user communities and not only in those. As an introduction, the major components of an UAS are identified. The paper focuses on a 1.1kg UAV which is integrated and gets applied on a day-to-day basis as part of an UAS in standard aerial imaging tasks for more than two years already. We present the unmanned airborne vehicle in some detail as well as the overall system components such as autopilot, ground station, flight mission planning and control, and first level image processing. The paper continues with reporting on experiences gained in setting up constraints such a system needs to fulfill. Further on, operational aspects with emphasis on unattended flight mission mode are presented. Various examples show the applicability of UAS in geospatial tasks, proofing that UAS are capable delivering reliably e.g. orthomosaics, digital surface models and more. Some remarks on achieved accuracies give an idea on obtainable qualities. A discussion about safety features puts some light on important matters when entering unmanned flying activities and rounds up this paper. Conclusions summarize the state of the art of an operational UAS from the point of the view of the author.
LATENCY DETERMINATION AND COMPENSATION IN REAL-TIME GNSS/INS INTEGRATED NAVIGATION SYSTEMS
Authors: Solomon P. D. , Wang J. , Rizos C.
Published: September 2012

Abstract. Unmanned Aerial Vehicle (UAV) technology is now commonplace in many defence and civilian environments. However, the high cost of owning and operating a sophisticated UAV has slowed their adoption in many commercial markets. Universities and research groups are actively experimenting with UAVs to further develop the technology, particularly for automated flying operations. The two main UAV platforms used are fixed-wing and helicopter. Helicopter-based UAVs offer many attractive features over fixed-wing UAVs, including vertical take-off, the ability to loiter, and highly dynamic flight. However the control and navigation of helicopters are significantly more demanding than those of fixed-wing UAVs and as such require a high bandwidth real-time Position, Velocity, Attitude (PVA) navigation system. In practical Real-Time Navigation Systems (RTNS) there are delays in the processing of the GNSS data prior to the fusion of the GNSS data with the INS measurements. This latency must be compensated for otherwise it degrades the solution of the navigation filter. This paper investigates the effect of latency in the arrival time of the GNSS data in a RTNS. Several test drives and flights were conducted with a low-cost RTNS, and compared with a high quality GNSS/INS solution. A technique for the real-time, automated and accurate estimation of the GNSS latency in low-cost systems was developed and tested. The latency estimates were then verified through cross-correlation with the time-stamped measurements from the reference system. A delayed measurement Extended Kalman Filter was then used to allow for the real-time fusing of the delayed measurements, and then a final system developed for on-the-fly measurement and compensation of GNSS latency in a RTNS.
THE UNMANNED MISSION AVIONICS TEST HELICIOPTER – A FLEXIBLE AND VERSATILE VTOL-UAS EXPERIMENTAL SYSTEM
Authors: Schulz H.-W.
Published: September 2012

Abstract. civil customers. These applications cover a wide spectrum from R&D programs for the military customer to special services for the civil customer. This paper focuses on the technical conversion of a commercially available VTOL-UAS to ESG's Unmanned Mission Avionics Test Helicopter (UMAT), its concept and operational capabilities. At the end of the paper, the current integration of a radar sensor is described as an example of the UMATs flexibility. The radar sensor is developed by the Fraunhofer Institute for High Frequency Physics and Radar Techniques (FHR). It is integrated by ESG together with the industrial partner SWISS UAV.
MOBILE 3D MAPPING WITH A LOW-COST UAV SYSTEM
Authors: Neitzel F. , Klonowski J.
Published: September 2012

Abstract. In this contribution it is shown how an UAV system can be built at low costs. The components of the system, the equipment as well as the control software are presented. Furthermore an implemented programme for photogrammetric flight planning and its execution are described. The main focus of this contribution is on the generation of 3D point clouds from digital imagery. For this web services and free software solutions are presented which automatically generate 3D point clouds from arbitrary image configurations. Possibilities of georeferencing are described whereas the achieved accuracy has been determined. The presented workflow is finally used for the acquisition of 3D geodata. On the example of a landfill survey it is shown that marketable products can be derived using a low-cost UAV.
AUTOMATIC ROAD GAP DETECTION USING FUZZY INFERENCE SYSTEM
Authors: Hashemi S. , Valadan Zoej M. J. , Mokhtarzadeh M.
Published: September 2012

Abstract. Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1) Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2) Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3) Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4) Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.
EFFICIENCY OF CONTEXT-BASED ATTRIBUTES FOR LAND-USE CLASSIFICATION OF URBAN ENVIRONMENTS
Authors: Hermosilla Gómez T. , Ruiz L. A. , Recio J. A. , Cambra López M.
Published: September 2012

Abstract. We present a study for the evaluation of the efficiency of context features in object-based land-use classification of urban environments using aerial high spatial resolution imagery and LiDAR data. Objects were defined by means of cartographic boundaries derived from the cadastral geospatial database. Objects are exhaustively described through different types of image derived features (i.e. spectral and texture), three-dimensional features computed from LiDAR data, and geometrical features describing the shape of each object. Additionally, the context of each object is described considering several aspects: adjacency, urban morphology, vegetation, and geometry. Adjacency between objects was characterized by features computed using the graph theory. Urban morphology features are related to the shape and size of neighbouring buildings, and are often related to their socio- economic function. The presence and density of vegetation are strongly related to the different urban typologies. Many of the contextual features are related to buildings, which are obtained by means of automatic building detection techniques. The meaning of the defined features, and their contribution to the classification accuracy were analyzed. The results showed that the inclusion of contextual features had a positive effect on land use classification of urban environments, increasing the overall accuracy around 4%, compared of using only the rest of features. The classification efficiency particularly increased in some classes, such as different typologies of suburban buildings, planned urban areas and historical areas.
TERRASAR-X AND RAPIDEYE DATA FOR THE PARAMETERISATION OF RELATIONAL CHARACTERISTICS OF URBAN ATKIS DLM OBJECTS
Authors: Hese S. , Lindner M. , Voltersen M. , Berger C.
Published: September 2012

Abstract. This work presents a multi-sensor data analysis concept for the parameterisation of urban landuse in comparison to ATKIS DLM reference objects (digital landscape model). An object based top-down approach is implemented and the potential of multisensor data for a primary urban landcover object classification is assessed. Urban landuse structure is developed based on relational features applied to land cover objects and compared to an aggregated DLM class legend. For a better imperviousness description an advanced imperviousness measure – the build-up impervious intensity ratio is developed that takes building height and derived floor area (based on LiDAR data) and the amount of vegetation within a search radius to every building into account. The developed concept is parameterized with test areas in Rostock and transferability is investigated with data coverage in Cologne (Germany). The work is linked to the urban work package of the DLR funded ENVILAND-2 project that aims to develop operational concepts for the use of TerraSAR-X and Rapideye data in urban mapping scenarios.
MULTITEMPORAL CROP TYPE CLASSIFICATION USING CONDITIONAL RANDOM FIELDS AND RAPIDEYE DATA
Authors: Hoberg T. , Müller S.
Published: September 2012

Abstract. The task of crop type classification with multitemporal imagery is nowadays often done applying classifiers that are originally developed for single images like support vector machines (SVM). These approaches do not model temporal dependencies in an explicit way. Existing approaches that make use of temporal dependencies are in most cases quite simple and based on rules. Approaches that integrate temporal dependencies to statistical models are very rare and at an early stage of development. Here our approach CRFmulti, based on conditional random fields (CRF), should make a contribution. Conditional random fields consider context knowledge among neighboring primitives in the same way as Markov random fields (MRF) do. Furthermore conditional random fields handle the feature vectors of the neighboring primitives and not only the class labels. Additional to taking spatial context into account, we present an approach for multitemporal data processing where a temporal association potential has been integrated to the common CRF approach to model temporal dependencies. The classification works on pixel ‐level using spectral image features, whereas all available single images are taken separately. For our experiments a high resolution RapidEye satellite data set of 2010 consisting of 4 images made during the whole vegetation period from April to October is taken. Six crop type categories are distinguished, namely grassland, corn, winter crop, rapeseed, root crops and other crops. To evaluate the potential of the new conditional random field approach the classification result is compared to a manual reference on pixel‐ and on object‐level. Additional a SVM approach is applied under the same conditions and should serve as a benchmark.
DELINEATING PARAMETERS FOR OBJECT-BASED URBAN STRUCTURE MAPPING IN SANTIAGO DE CHILE USING QUICKBIRD DATA
Authors: Huck A. , Hese S. , Banzhaf E.
Published: September 2012

Abstract. This work aims to parameterize the urban structure types (UST) in Santiago de Chile on statistical block level. In connotation of remote sensing UST are defined as land-use structure entities. Central input data for this object-oriented approach is spatially very high resolution panfused and atmospherically corrected Quickbird data. To analyse and assess the structural properties of urban land- cover objects within block level entities, basic and robust land-cover class descriptions are developed. For enhanced class descriptions several image object scales are created. Based on defined UST and additional field data a set of test areas is selected for four municipalities assigned to different socio-spatial clusters in Santiago de Chile. In all test areas the distribution of the basic land- cover classes is parameterized using complex sub-object and relational image object descriptions. The central features to characterise the UST in this study are percentage area and density of subscale land-cover class objects. To carry out this analysis, the expert knowledge on UST is valuable to choose specific reference objects within the statistical block level. After the concept is implemented at the smallest scale, the approach can successfully be applied to the whole municipality once specific structural information are aggregated. The work is linked to activities of the project Risk Habitat Megacity and developed in close cooperation with the Helmholtz Centre for Environmental Research – UFZ in Leipzig, Germany. Finally, the resulting land-use structure entities will be linked to socio-spatial characteristics in the above mentioned cluster with respect to urban vulnerability.
POTENTIALS OF IMAGE BASED ACTIVE RANGING TO CAPTURE DYNAMIC SCENES
Authors: Jutzi B. , Weinmann M. , Runge P. , Jutzi B.
Published: September 2012

Abstract. Obtaining a 3D description of man-made and natural environments is a basic task in Computer Vision and Remote Sensing. To this end, laser scanning is currently one of the dominating techniques to gather reliable 3D information. The scanning principle inherently needs a certain time interval to acquire the 3D point cloud. On the other hand, new active sensors provide the possibility of capturing range information by images with a single measurement. With this new technique image-based active ranging is possible which allows capturing dynamic scenes, e.g. like walking pedestrians in a yard or moving vehicles. Unfortunately most of these range imaging sensors have strong technical limitations and are not yet sufficient for airborne data acquisition. It can be seen from the recent development of highly specialized (far-)range imaging sensors – so called flash-light lasers – that most of the limitations could be alleviated soon, so that future systems will be equipped with improved image size and potentially expanded operating range. The presented work is a first step towards the development of methods capable for application of range images in outdoor environments. To this end, an experimental setup was set up for investigating these proposed possibilities. With the experimental setup a measurement campaign was carried out and first results will be presented within this paper.
DEM GENERATION FROM HIGH RESOLUTION SATELLITE IMAGES THROUGH A NEW 3D LEAST SQUARES MATCHING ALGORITHM
Authors: Kim T. , Rhee S.
Published: September 2012

Abstract. Automated generation of digital elevation models (DEMs) from high resolution satellite images (HRSIs) has been an active research topic for many years. However, stereo matching of HRSIs, in particular based on image-space search, is still difficult due to occlusions and building facades within them. Object-space matching schemes, proposed to overcome these problem, often are very time consuming and critical to the dimensions of voxels. In this paper, we tried a new least square matching (LSM) algorithm that works in a 3D object space. The algorithm starts with an initial height value on one location of the object space. From this 3D point, the left and right image points are projected. The true height is calculated by iterative least squares estimation based on the grey level differences between the left and right patches centred on the projected left and right points. We tested the 3D LSM to the Worldview images over 'Terrassa Sud' provided by the ISPRS WG I/4. We also compared the performance of the 3D LSM with the correlation matching based on 2D image space and the correlation matching based on 3D object space. The accuracy of the DEM from each method was analysed against the ground truth. Test results showed that 3D LSM offers more accurate DEMs over the conventional matching algorithms. Results also showed that 3D LSM is sensitive to the accuracy of initial height value to start the estimation. We combined the 3D COM and 3D LSM for accurate and robust DEM generation from HRSIs. The major contribution of this paper is that we proposed and validated that LSM can be applied to object space and that the combination of 3D correlation and 3D LSM can be a good solution for automated DEM generation from HRSIs.
MAPPING UNDERSTORY TREES USING AIRBORNE DISCRETE-RETURN LIDAR DATA
Authors: Korpela I. , Hovi A. , Morsdorf F.
Published: September 2012

Abstract. Understory trees in multi-layer stands are often ignored in forest inventories. Information about them would benefit silviculture, wood procurement and biodiversity management. Cost-efficient inventory methods for the assessment of the presence, density, species- and size-distributions are called for. LiDAR remote sensing is a promising addition to field work. Unlike in passive image data, in which the signals from multiple layers mix, the 3D position of each hot-spot reflection is known in LiDAR data. The overstory however prevents from obtaining a wall-to-wall sample of understory, and measurements are subject to transmission losses. Discriminating between the crowns of dominant and suppressed trees can also be challenging. We examined the potential of LiDAR for the mapping of the understory trees in Scots pine stands (62°N, 24°E), using carefully georeferenced reference data and several LiDAR data sets. We present results that highlight differences in echo-triggering between sensors that affect the near-ground height data. A conceptual model for the transmission losses in the overstory was created and formulated into simple compensation models that reduced the intensity variation in second- and third return data. The task is highly ill-posed in discrete-return LiDAR data, and our models employed the geometry of the overstory as well as the intensity of previous returns. We showed that even first-return data in the understory is subject to losses in the overstory that did not trigger an echo. Even with compensation of the losses, the intensity data was deemed of low value in species discrimination. Area-based LiDAR height metrics that were derived from the data belonging to the crown volume of the understory showed reasonable correlation with the density and mean height of the understory trees. Assessment of the species seems out of reach in discrete-return LiDAR data, which is a drastic drawback.
MORPHOLOGICAL FILLING OF DIGITAL ELEVATION MODELS
Authors: Krauß T. , d'Angelo P.
Published: September 2012

Abstract. In this paper a new approach for a more detailed post processing and filling of digital elevation models (DEMs) in urban areas is presented. To reach the required specifications in a first step the errors in digital surface models (DSMs) generated by dense stereo algorithms are analyzed and methods for detection and classification of the different types of errors are implemented. Subsequently the classified erroneous areas are handled in separate manner to eliminate outliers and fill the DSM properly. The errors which can be detected in DSMs range from outliers – single pixels or small areas containing extremely high or low values – over noise from mismatches, single small holes to occlusions, where large areas are not visible in one of the images of the stereo pair. To validate the presented method artificial DSMs are generated and superimposed with all different kinds of described errors like noise (small holes cut in), outliers (small areas moved up/down), occlusions (larger areas beneath steep walls) and so on. The method is subsequently applied to the artificial DSMs and the resulting filled DSMs are compared to the original artificial DSMs without the introduced errors. Also the method is applied to stereo satellite generated DSMs from the ISPRS Comission 1 WG4 benchmark dataset and the results are checked with the also provided first pulse laser DSM data. Finally the results are discussed, strengths and weaknesses of the approach are shown and suggestions for application and optimization are given.
EXTRACTING ROOF PARAMETERS AND HEAT BRIDGES OVER THE CITY OF OLDENBURG FROM HYPERSPECTRAL, THERMAL, AND AIRBORNE LASER SCANNING DATA
Authors: Bannehr L. , Luhmann Th. , Piechel J. , Roelfs T. , Schmidt An.
Published: September 2012

Abstract. Remote sensing methods are used to obtain different kinds of information about the state of the environment. Within the cooperative research project HiReSens, funded by the German BMBF, a hyperspectral scanner, an airborne laser scanner, a thermal camera, and a RGB-camera are employed on a small aircraft to determine roof material parameters and heat bridges of house tops over the city Oldenburg, Lower Saxony. HiReSens aims to combine various geometrical highly resolved data in order to achieve relevant evidence about the state of the city buildings. Thermal data are used to obtain the energy distribution of single buildings. The use of hyperspectral data yields information about material consistence of roofs. From airborne laser scanning data (ALS) digital surface models are inferred. They build the basis to locate the best orientations for solar panels of the city buildings. The combination of the different data sets offers the opportunity to capitalize synergies between differently working systems. Central goals are the development of tools for the collection of heat bridges by means of thermal data, spectral collection of roofs parameters on basis of hyperspectral data as well as 3D-capture of buildings from airborne lasers scanner data. Collecting, analyzing and merging of the data are not trivial especially not when the resolution and accuracy is aimed in the domain of a few decimetre. The results achieved need to be regarded as preliminary. Further investigations are still required to prove the accuracy in detail.
GENERALIZED SPATIAL MODELS OF FOREST STRUCTURE USING AIRBORNE MULTISPECTRAL AND LASER SCANNER DATA
Authors: Latifi H. , Koch B.
Published: September 2012

Abstract. Forest structure was modelled to explore the nonparametric approaches for inventory of small forests. Models were built using information from airborne optical and LiDAR data across two adjacent forest sites using the remote sensing data collected by a similar instrument. Off-site samples from the inventoried reference stands were combined with drawn samples from the target area for modelling responses. The applied combinations included two imputation methods, three sampling designs, and two predictor subset sizes, where a Genetic Algorithm (GA) was used to prune the predictors. Diagnostic tools including Root Mean Square Error, bias and Standard Error of Imputation were employed to evaluate the results. Results showed that Random Forests produced more accurate results than Most Similar Neighbour inference, yet was slightly more biased than MSN. Appending systematically stratified samples from the target dataset yielded more accurate results, yet it was most influential up to a low-to-medium intensity. The use of pruned predictors resulted in reduced bias. By bootstrapping the RMSE, the majority of the simulations lied within the confidence intervals. The applied methods show positive potential towards producing spatial models of forest structure.
THE EFFECT OF LOSSY IMAGE COMPRESSION ON OBJECT BASED IMAGE CLASSIFICATION – WORLDVIEW-2 CASE STUDY
Authors: Marsetic A. , Kokalj Z. , Ostir K.
Published: September 2012

Abstract. Lossy compression is becoming increasingly used in remote sensing although its effect on the processing results has yet not been fully investigated. This paper presents the effects of JPEG 2000 lossy compression on the classification of very high-resolution WorlView-2 imagery. The k-nearest neighbor and support vector machine methods of the object based classification were used and compared. The results explore the impact of compression on the images, segmentation and resulting classification. The study proves that in general lossy compression does not adversely affect the classification of images; what is more, in some cases classification of compressed images gives better results than classification of the original image. Classification accuracy of support vector machines method indicates that compression ratios of up to 30:1 can be used without any loss of accuracy. The best result of the k-nearest neighbor method was obtained with the highest compression ratio (100:1), but the outcome cannot be trusted without reserve. In the study we found that the support vector machine method gives better classification results than the k-nearest neighbor and is also recommended for further research. In addition to the classification method, image segmentation, a basic step of object classification, plays an important role in the accuracy of the results.
PHOTO-BASED 3D SCANNING VS. LASER SCANNING – COMPETITIVE DATA ACQUISITION METHODS FOR DIGITAL TERRAIN MODELLING OF STEEP MOUNTAIN SLOPES
Authors: Kolecka N.
Published: September 2012

Abstract. The paper presents how terrestrial laser scanning (TLS) and terrestrial digital photogrammetry were used to create a 3D model of a steep mountain wall. Terrestrial methods of data acquisition are the most suitable for such relief, as the most effective registration is perpendicular to the surface. First, various aspects of photo-based scanning and laser scanning were discussed. The general overview of both technologies was followed by the description of a case study of the western wall of the Kościelec Mountain (2155 m). The case study area is one of the most interesting and popular rock climbing areas in the Polish High Tatra Mts. The wall is about 300 meters high, has varied relief and some parts are overhung. Triangular irregular mesh was chosen to represent the true- 3D surface with its complicated relief. To achieve a more smooth result for visualization NURBS curves and surfaces were utilized. Both 3D models were then compared to the standard DTM of the Tatra Mountains in TIN format, obtained from aerial photographs (0.2 m ground pixel size). The results showed that both TLS and terrestrial photogrammetry had similar accuracy and level of detail and could effectively supplement very high resolution DTMs of the mountain areas.
ACCURATE MATCHING AND RECONSTRUCTION OF LINE FEATURES FROM ULTRA HIGH RESOLUTION STEREO AERIAL IMAGES
Authors: Ok A. O. , Wegner J. D. , Heipke C. , Rottensteiner F. , Soergel U. , Toprak V.
Published: September 2012

Abstract. In this study, a new reconstruction approach is proposed for the line segments that are nearly-aligned (≤ 10º) with the epipolar line. The method manipulates the redundancy inherent in line pair-relations to generate artificial 3D point entities and utilize those entities during the estimation process to improve the height values of the reconstructed line segments. The best point entities for the reconstruction are selected based on a newly proposed weight function. To test the performance of the proposed approach, we selected three test patches over a built up area of the city of Vaihingen – Germany. Based on the results, the proposed approach produced highly promising reconstruction results for the line segments that are nearly-aligned with the epipolar line.
HYBRID AUTOMATIC BUILDING INTERPRETATION SYSTEM
Authors: Pakzad K. , Klink A. , Müterthies A. , Gröger G. , Stroh V. , Plümer L.
Published: September 2012

Abstract. HABIS (Hybrid Automatic Building Interpretation System) is a system for an automatic reconstruction of building roofs used in virtual 3D building models. Unlike most of the commercially available systems, HABIS is able to work to a high degree automatically. The hybrid method uses different sources intending to exploit the advantages of the particular sources. 3D point clouds usually provide good height and surface data, whereas spatial high resolution aerial images provide important information for edges and detail information for roof objects like dormers or chimneys. The cadastral data provide important basis information about the building ground plans. The approach used in HABIS works with a multi-stage-process, which starts with a coarse roof classification based on 3D point clouds. After that it continues with an image based verification of these predicted roofs. In a further step a final classification and adjustment of the roofs is done. In addition some roof objects like dormers and chimneys are also extracted based on aerial images and added to the models. In this paper the used methods are described and some results are presented.
DSM GENERATION FROM HIGH RESOLUTION COSMO-SKYMED IMAGERY WITH RADARGRAMMETRIC MODEL
Authors: Capaldo P. , Crespi M. , Fratarcangeli F. , Nascetti A. , Pieralice F.
Published: September 2012

Abstract. The availability of new high resolution radar spaceborne sensors offers new interesting potentialities for the geomatics application: spatial and temporal change detection, features extraction, generation of Digital Surface (DSMs). As regards the DSMs generation from new high resolution data (as SpotLight imagery), the development and the accuracy assessment of method based on radargrammetric approach are topics of great interest and relevance. The aim of this investigation is the DSM generation from a COSMO-SkyMed Spotlight stereo pair with the radargrammetric technique. DSM generation procedure consists of two basic steps: the stereo pair orientation and the image matching. The suite for radargrammetric approach has been implemented in SISAR (Software per Immagini Satellitari ad Alta Risoluzione), a scientific software developed at the Geodesy and Geomatic Institute of the University of Rome "La Sapienza". As regard the image matching the critical issue is the definition of a strategy to search the corresponding points; in SISAR software, an original matching procedure has been developed, based on a coarse-to-fine hierarchical solution with an effective combination of geometrical constrains and an Area Base Matching (ABM) algorithm.
REFINEMENT OF DIGITAL SURFACE MODELS THROUGH CONSTRAINED CONNECTIVITY PARTITIONING OF OPTICAL IMAGERY
Authors: Poli D. , Soille P.
Published: September 2012

Abstract. One of the tasks of ISFEREA action at the Joint Research Center (JRC) in Ispra, Italy, is to assess changes in land cover after natural disasters using satellite imagery. To describe the pre-event and post-event scenarios the available heterogeneous data are analysed and eventually fused in order to achieve the most accurate and reliable information. With respect to automatic 3D information extraction, the availability of accurate and detailed Digital Surface Models (DSMs) is a crucial for automatic building detection and subsequent damage estimation. This paper proposes a methodology for the geometric refinement of an arbitrary DSM using high or very high resolution satellite scenes. The main idea is to fuse the DSM and the scene through an advanced hierarchical image partitioning. The method is applied on a dataset over Catalonia, Spain, provided by ISPRS WGI/4 through the project "Benchmarking and quality analysis of DEM generated from high and very high resolution optical stereo satellite data". During this experiment a WorldView-1 quasi-nadir scene was used for the enhancement of the DSM generated by a Cartosat-1 stereopair. The original and final DSM are compared to the Lidar DSM on the same area for quality analysis. After the description of the methodology, the first results are reported and commented.
POTENTIAL EVALUATION OF DIFFERENT TYPES OF IMAGES AND THEIR COMBINATION FOR THE CLASSIFICATION OF GIS OBJECTS CROPLAND AND GRASSLAND
Authors: Recio J. A. , Helmholz P. , Müller S.
Published: September 2012

Abstract. In many publications the performance of different classification algorithms regarding to agricultural classes is evaluated. In contrast, this paper focuses on the potential of different imagery for the classification of the two most frequent classes: cropland and grassland. For our experiments three categories of imagery, high resolution aerial images, high resolution RapidEye satellite images and medium resolution Disaster Monitoring Constellation (DMC) satellite images are examined. An object-based image classification, as one of the most reliable methods for the automatic updating and evaluation of landuse geospatial databases, is chosen. The object boundaries are taken from a GIS database, each object is described by means of a set of image based features. Spectral, textural and structural (semivariogram derived) features are extracted from images of different dates and sensors. During classification a supervised decision trees generating algorithm is applied. To evaluate the potential of the different images, all possible combinations of the available image data are tested during classification. The results show that the best performance of landuse classification is based on RapidEye data (overall accuracy of 90%), obtaining slightly accuracy increases when this imagery is combined with additional image data (overall accuracy of 92%).
MORPHOLOGIC QUALITY OF DSMs BASED ON OPTICAL AND RADAR SPACE IMAGERY
Authors: Sefercik U. G. , Bayik C. , Karakis S. , Jacobsen K.
Published: September 2012

Abstract. Digital Surface Models (DSMs) are representing the visible surface of the earth by the height corresponding to its X-, Y-location and height value Z. The quality of a DSM can be described by the accuracy and the morphologic details. Both depend upon the used input information, the used technique and the roughness of the terrain. The influence of the topographic details to the DSM quality is shown for the test fields Istanbul and Zonguldak. Zonguldak has a rough mountainous character with heights from sea level up to 1640m, while Istanbul is dominated by rolling hills going up to an elevation of 435m. DSMs from SPOT-5, the SRTM C-band height models and ASTER GDEM have been investigated. The DSMs have been verified with height models from large scale aerial photos being more accurate and including morphologic details. It was necessary to determine and respect shifts of the height models caused by datum problems and orientation of the height models. The DSM quality is analyzed depending upon the terrain inclination. The DSM quality differs for both test fields. The morphologic quality depends upon the point spacing of the analyzed DSMs and the terrain characteristics.
CRUCIAL POINTS OF INTERFEROMETRIC PROCESSING FOR DEM GENERATION USING HIGH RESOLUTION SAR DATA
Authors: Sefercik U. G. , Dana I.
Published: September 2012

Abstract. Data collection for digital elevation model (DEM) generation can be carried out by two main methods in space-borne remote sensing such as stereoscopy using optical or radar satellite imagery (stereophotogrammetry, respectively radargrammetry) and interferometry based on interferometric synthetic aperture radar (InSAR) data. These techniques have advantages and disadvantages in comparison against each other. Especially filling the gaps which arise from the problem of cloud coverage in DEM generation by optical imagery, InSAR became operational in recent years and DEMs became the most demanded interferometric products. Essentially, in comparison, DEM generation from synthetic aperture radar (SAR) images is not a simple manner like generation from optical satellite imagery. Interferometric processing has several complicated steps for the production of a DEM. The quality of the data set and used software package come into prominence for the stability of the generated DEM. In the paper, the interferometric processing steps for DEM generation from InSAR data and the crucial threshold values are tried to be explained. For DEM generation, a part of Istanbul (historical peninsula and near surroundings) was selected as the test field because of data availability. The data sets of two different imaging modes (StripMap ~ 3 m resolution and High Resolution Spotlight ~ 1 m resolution) of TerraSAR-X have been used. At the implementation, besides the determination of crucial points at interferometric processing steps, to define the effect of computer software, DEM production have been performed using two different software packages in parallel and the products have been compared In the result section of the paper, besides the colorful visualizations of final products along with the height scales, accuracy evaluations have been performed for both DEMs with the help of a more accurate reference digital terrain model (DTM). This reference model has been achieved by large scale aerial photos. Normally, it has a 5 m original grid spacing, however it has been resampled at a spacing of 1 m towards the needs of the research.
THE USE OF ACTIVE CONTOURS FOR THE DETECTION OF COASTLINES IN SAR IMAGES: A MODULAR KNOWLEDGE-BASED FRAMEWORK
Authors: Seppke B. , Dreschler-Fischer L. , Brauer M.
Published: September 2012

Abstract. Over the last years, active contour methods have become a basic tool in computer vision. They have proven to be efficient for various image processing applications, like reconstruction of the edges inside images or the tracing of image features. However, when applying the basic snake technique to synthetic aperture radar (SAR) remote sensing images, the detection of edges may not be satisfactory. This is caused by the special imaging technique of SAR that may tend to produce varying-contrast edges and the commonly known speckle noise. In (Seppke et al., 2010) we proposed the use of asymmetric external energy terms to cope with these problems. In this paper we extend our approach and present a modular framework for the application of snake algorithms to SAR imagery. The main emphasis of the framework is the use of higher knowledge about the scene depicted, e.g to initialize the snake with suitable parameters. Another objective is to establish a modular designed and thus highly ﬂexible testbed for the comparison of different active contour approaches. We present the framework’s design and preliminary results for the detection of coastlines in SAR images. The proposed framework has already proven to be a valuable tool for both, the interpretation and understanding of the results. For future projects, the framework will be used to investigate and compare the results of snakes when applied to hi-resolution SAR imagery, e.g. TerraSAR-X HR Spotlight images.