bluuebunny/crossref_metadata_2025 huggingface dataset:
DATA FUSION FOR BUILDING RECONSTRUCTION FROM MULTI-ASPECT INSAR DATA
Authors: Maksymiuk O. , Stilla U.
Published: May 2013

Abstract. In this paper we present a novel approach for 3D reconstruction of point clouds based on single baseline Multi-Aspect InSAR (MASAR) data. The point clouds represent an intermediate result to achieve a comprehensive building reconstruction framework. The exact determination of scatterers based on SAR data is a non-trivial task since the optimal solution requires the knowledge of the number of scatterers within one range cell. In recent years many methods were proposed addressing this problem but most of them require multiple observations making them inapplicable to our task. We use a Probabilistic Graphical Model (PGM) to combine all aspects into a common framework exploiting all contradictions and redundancy in the data. The model is used iteratively together with local optimizations adjusting the hypothesis of the scatterer within one range cell to the corresponding observation. This makes it possible to find a global solution.
HOW TO PAN-SHARPEN IMAGES USING THE GRAM-SCHMIDT PAN-SHARPEN METHOD – A RECIPE
Authors: Maurer T.
Published: May 2013

Abstract. Since its publication in 1998 (Laben and Brower, 2000), the Gram-Schmidt pan-sharpen method has become one of the most popular algorithms to pan-sharpen multispectral (MS) imagery. It outperforms most other pan-sharpen methods in both maximizing image sharpness and minimizing color distortion. It is, on the other hand, also more complex and computationally expensive than most other methods, as it requires forward and backward transforming the entire image. Another complication is the lack of a clear recipe of how to compute the sensor dependent MS to Pan weights that are needed to compute the simulated low resolution pan band. Estimating them from the sensor’s spectral sensitivity curves (in different ways), or using linear regression or least square methods are typical candidates which can include other degrees of freedom such as adding a constant offset or not. As a result, most companies and data providers do it somewhat differently. Here we present a solution to both problems. The transform coefficients can be computed directly and in advance from the MS covariance matrix and the MS to Pan weights. Once the MS covariance matrix is computed and stored with the image statistics, any small section of the image can be pan-sharpened on the fly, without having to compute anything else over the entire image. Similarly, optimal MS to Pan weights can be computed directly from the full MS-Pan covariance matrix, guaranteeing optimal image quality and consistency.
TRANSMISSION LINE THEORY BASED TWO LAYER MODEL FOR DETERMINING SOIL MOISTURE
Authors: Mishra P. , Singh D.
Published: May 2013

Abstract. Present paper deals with the task of estimating soil moisture under vegetation cover by using transmission line theory based two layer model. The two layer model measures the impedance of both the layers namely, soil and vegetation. This impedance is the function of dielectric constant and thickness of both the layers. For known dielectric constant and height of vegetation layer, dielectric constant of soil was determined for certain thickness of soil layer using genetic algorithm (GA). The soil moisture value was retrieved from dielectric constant of soil by using Topp et al., (1980) relationship. Retrieved soil moisture values were in good agreement with observed values.
SYNERGETICS FRAMEWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION
Authors: Müller R. , Cerra D. , Reinartz P.
Published: May 2013

Abstract. In this paper a new classification technique for hyperspectral data based on synergetics theory is presented. Synergetics – originally introduced by the physicist H. Haken – is an interdisciplinary theory to find general rules for pattern formation through selforganization and has been successfully applied in fields ranging from biology to ecology, chemistry, cosmology, and thermodynamics up to sociology. Although this theory describes general rules for pattern formation it was linked also to pattern recognition. Pattern recognition algorithms based on synergetics theory have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analysed independently. The classification scheme based on synergetics introduces also methods for spatial regularization to get rid of "salt and pepper" classification results and for iterative parameter tuning to optimize class weights. The paper reports an experiment on a benchmark data set frequently used for method comparisons. This data set consists of a hyperspectral scene acquired by the Airborne Visible Infrared Imaging Spectrometer AVIRIS sensor of the Jet Propulsion Laboratory acquired over the Salinas Valley in CA, USA, with 15 vegetation classes. The results are compared to state-of-the-art methodologies like Support Vector Machines (SVM), Spectral Information Divergence (SID), Neural Networks, Logistic Regression, Factor Graphs or Spectral Angle Mapper (SAM). The outcomes are promising and often outperform state-of-the-art classification methodologies.
ESTIMATION OF BIOMASS POTENTIAL BASED ON CLASSIFICATION AND HEIGHT INFORMATION
Authors: Müller S. , Büscher O. , Jandewerth M.
Published: May 2013

Abstract. On the way to make energy supply independent from fossil resources more and more renewable energy sources have to be explored. Biomass has become an important energy resource during the last years and the consumption is rising steadily. Common sources of biomass are agricultural production and forestry but the production of these sources is stagnating due to limited space. To explore new sources of biomass like in the field of landscape conservation the location and available amount of biomass is unknown. Normally, there are no reliable data sources to give information about the objects of interest such as hedges, vegetation along streets, railways and rivers, field margins and ruderal sites. There is a great demand for an inventory of these biomass sources which could be answered by applying remote sensing technology. As biomass objects considered here are sometimes only a few meters wide, spectral unmixing is applied to separate different material mixtures reflected in one image pixel. The spectral images are assumed to have a spatial resolution of 5–20 m with multispectral or hyperspectral band configurations. Combining the identified material part fractions with height information and GIS data afterwards will give estimates about the location of biomass objects. The method is applied to test data of a Sentinel-2 simulation and the results are evaluated visually.
RADIOMETRIC BLOCK ADJUSMENT AND DIGITAL RADIOMETRIC MODEL GENERATION
Authors: Pros A. , Colomina I. , Navarro J. A. , Antequera R. , Andrinal P.
Published: May 2013

Abstract. In this paper we present a radiometric block adjustment method that is related to geometric block adjustment and to the concept of a terrain Digital Radiometric Model (DRM) as a complement to the terrain digital elevation and surface models. A DRM, in our concept, is a function that for each ground point returns a reflectance value and a Bidirectional Reflectance Distribution Function (BRDF). In a similar way to the terrain geometric reconstruction procedure, given an image block of some terrain area, we split the DRM generation in two phases: radiometric block adjustment and DRM generation. In the paper we concentrate on the radiometric block adjustment step, but we also describe a preliminary DRM generator. In the block adjustment step, after a radiometric pre-calibraton step, local atmosphere radiative transfer parameters, and ground reflectances and BRDFs at the radiometric tie points are estimated. This radiometric block adjustment is based on atmospheric radiative transfer (ART) models, pre-selected BRDF models and radiometric ground control points. The proposed concept is implemented and applied in an experimental campaign, and the obtained results are presented. The DRM and orthophoto mosaics are generated showing no radiometric differences at the seam lines.
AUTOMATIC ORIENTATION OF LARGE BLOCKS OF OBLIQUE IMAGES
Authors: Rupnik E. , Nex F. , Remondino F.
Published: May 2013

Abstract. Nowadays, multi-camera platforms combining nadir and oblique cameras are experiencing a revival. Due to their advantages such as ease of interpretation, completeness through mitigation of occluding areas, as well as system accessibility, they have found their place in numerous civil applications. However, automatic post-processing of such imagery still remains a topic of research. Configuration of cameras poses a challenge on the traditional photogrammetric pipeline used in commercial software and manual measurements are inevitable. For large image blocks it is certainly an impediment. Within theoretical part of the work we review three common least square adjustment methods and recap on possible ways for a multi-camera system orientation. In the practical part we present an approach that successfully oriented a block of 550 images acquired with an imaging system composed of 5 cameras (Canon Eos 1D Mark III) with different focal lengths. Oblique cameras are rotated in the four looking directions (forward, backward, left and right) by 45° with respect to the nadir camera. The workflow relies only upon open-source software: a developed tool to analyse image connectivity and Apero to orient the image block. The benefits of the connectivity tool are twofold: in terms of computational time and success of Bundle Block Adjustment. It exploits the georeferenced information provided by the Applanix system in constraining feature point extraction to relevant images only, and guides the concatenation of images during the relative orientation. Ultimately an absolute transformation is performed resulting in mean re-projection residuals equal to 0.6 pix.
USING LATTICE TOPOLOGY INFORMATION TO INVESTIGATE PERSISTENT SCATTERERS AT FACADES IN URBAN AREAS
Authors: Schack L. , Soergel U.
Published: May 2013

Abstract. Modern spaceborne SAR sensors like TerraSAR-X offer ground resolution of up to one meter in range and azimuth direction. Buildings, roads, bridges, and other man-made structures appear in such data often as regular patterns of strong and temporally stable points (Persistent Scatterer, PS). As one step in the process of unveiling what object structure actually causes the PS (i.e., physical nature) we compare those regular structures in SAR data to their correspondences in optical imagery. We use lattices as a common data representation for visible facades. By exploiting the topology information given by the lattices we can complete gaps in the structures which is one step towards the understanding of the complex scattering characteristics of distinct facade objects.
MONITORING CONCEPTS FOR COASTAL AREAS USING LIDAR DATA
Authors: Schmidt A. , Rottensteiner F. , Soergel U.
Published: May 2013

Abstract. Coastal areas are characterized by high spatial and temporal variability. In order to detect undesired changes at early stages, enabling rapid countermeasures to mitigate or minimize potential harm or hazard, a recurrent monitoring becomes necessary. In this paper, we focus on two monitoring task: the analysis of morphological changes and the classification and mapping of habitats. Our concepts are solely based on airborne lidar data which provide substantial information in coastal areas. For the first task, we generate a digital terrain model (DTM) from the lidar point cloud and analyse the dynamic of an island by comparing the DTMs of different epochs with a time difference of six years. For the deeper understanding of the habitat composition in coastal areas, we classify the lidar point cloud by a supervised approach based on Conditional Random Fields. From the classified point cloud, water-land-boundaries as well as mussel bed objects are derived afterwards. We evaluate our approaches on two datasets of the German Wadden Sea.
LABELING OF CLUSTERS BASED ON CRITICAL ANALYSIS OF TEXTURE MEASURES
Authors: Singh D. , Vijaya Chamundeeswari V.
Published: May 2013

Abstract. It is well known that unsupervised classification of a single polarized SAR image is accomplished mainly by two steps i.e., (i) Clustering the SAR image into groups or clusters on the basis of backscattering coefficient and textures present in SAR image, and (ii) Labeling the various clusters in their respective class (For example, land cover types such as water, urban, agriculture or any other areas). In this context, labeling is termed as naming the various clusters or groups of pixels according to nature of the terrain as a certain land cover type it belongs to. Labeling of various clusters is a crucial and important aspect to identify various clusters in their original class (here, land cover class is assumed as class, whereas, in general, class may refer to any group of targets). It is still a challenging task to label the cluster without any a priori information. So, it is important to develop such a technique by which clusters can be labeled according to their class. Therefore, focus in this paper is to induct surface roughness with backscattering coefficient to label various clusters (i.e. major land cover types). We have proposed an empirical relation to estimate roughness parameters from the SAR image. Labeling of clusters was carried out on the basis of roughness parameters and backscattering coefficient.
AUTOMATIC DETECTION AND RECOGNITION OF MAN-MADE OBJECTS IN HIGH RESOLUTION REMOTE SENSING IMAGES USING HIERARCHICAL SEMANTIC GRAPH MODEL
Authors: Sun X. , Thiele A. , Hinz S. , Fu K.
Published: May 2013

Abstract. In this paper, we propose a hierarchical semantic graph model to detect and recognize man-made objects in high resolution remote sensing images automatically. Following the idea of part-based methods, our model builds a hierarchical possibility framework to explore both the appearance information and semantic relationships between objects and background. This multi-levels structure is promising to enable a more comprehensive understanding of natural scenes. After training local classifiers to calculate parts properties, we use belief propagation to transmit messages quantitatively, which could enhance the utilization of spatial constrains existed in images. Besides, discriminative learning and generative learning are combined interleavely in the inference procedure, to improve the training error and recognition efficiency. The experimental results demonstrate that this method is able to detect manmade objects in complicated surroundings with satisfactory precision and robustness.
RIGOROUS GEOREFERENCING OF ALSAT-2A PANCHROMATIC AND MULTISPECTRAL IMAGERY
Authors: Boukerch I. , Hadeid M. , Mahmoudi R. , Takarli B. , Hasni K.
Published: May 2013

Abstract. The exploitation of the full geometric capabilities of the High-Resolution Satellite Imagery (HRSI), require the development of an appropriate sensor orientation model. Several authors studied this problem; generally we have two categories of geometric models: physical and empirical models. Based on the analysis of the metadata provided with ALSAT-2A, a rigorous pushbroom camera model can be developed. This model has been successfully applied to many very high resolution imagery systems. The relation between the image and ground coordinates by the time dependant collinearity involving many coordinates systems has been tested. The interior orientation parameters must be integrated in the model, the interior parameters can be estimated from the viewing angles corresponding to the pointing directions of any detector, these values are derived from cubic polynomials provided in the metadata. The developed model integrates all the necessary elements with 33 unknown. All the approximate values of the 33 unknowns parameters may be derived from the informations contained in the metadata files provided with the imagery technical specifications or they are simply fixed to zero, so the condition equation is linearized and solved using SVD in a least square sense in order to correct the initial values using a suitable number of well-distributed GCPs. Using Alsat-2A images over the town of Toulouse in the south west of France, three experiments are done. The first is about 2D accuracy analysis using several sets of parameters. The second is about GCPs number and distribution. The third experiment is about georeferencing multispectral image by applying the model calculated from panchromatic image.
RAPID GEOMETRIC CORRECTION OF SSC TERRASAR-X IMAGES WITH DIRECT GEOREFERENCING, GLOBAL DEM AND GLOBAL GEOID MODELS
Authors: Vassilaki D. I. , Stamos A. A. , Ioannidis C.
Published: May 2013

Abstract. In this paper a process for rapid geometric correction of slant range SAR images is presented. The process is completely independent of ground control information thanks to the direct georeferencing method capabilities offered by the TerraSAR-X sensor. The process is especially rapid due to the use of readily available global DEMs and global geoid models. An additional advantage of this process is its flexibility. If a more accurate local DEM or local geoid model is readily available it can be used instead of the global DEM or global geoid model. The process is applied to geometrically correct a SSC TerraSAR-X image over a sub-urban mountainous area using the SRTM and the ASTER global DEMs and the EGM2008 global geoid model. Additionally two local, more accurate DEMs, are used. The accuracy of the process is evaluated by independent check points.
EVALUATION OF SAR DATA AS SOURCE OF GROUND CONTROL INFORMATION: FIRST RESULTS
Authors: Vassilaki D. I. , Ioannidis C , Stamos A. A.
Published: May 2013

Abstract. The high resolution imaging modes of modern SAR sensors has made SAR data compatible with optical images. SAR data offers various capabilities which can enhance the geometric correction process of optical images (accurate, direct and ground-independent georeferencing capabilities and global DEM products). In this paper the first results of an on-going study on the evaluation of SAR data as source of ground control information for the georeferencing of optical images are presented. The georeferencing of optical images using SAR data is in fact a co-registration problem which involves multimodal, mutitemporal, and multiresolution data. And although 2D transformations have proved to be insufficient for the georeferencing process, as they can not account for the distortions due to terrain, quite a few approaches on the registration of optical to SAR data using 2D-2D transformations can still be found in the literature. In this paper the performance of 2D-2D transformations is compared to the 3D-2D projective transformation over a greater area of Earth’s surface with arbitrary terrain type. Two alternative forms of ground control information are used: points and FFLFs. The accuracy of the computed results is obtained using independent CPs and it is compared to the geolocation accuracy specification of the optical image, as well as to the accuracy of exhaustive georeferencing done by third parties.
AUTOMATIC FEATURE-BASED POINT CLOUD REGISTRATION FOR A MOVING SENSOR PLATFORM
Authors: Weinmann M. , Dittrich A. , Hinz S. , Jutzi B.
Published: May 2013

Abstract. The automatic and accurate alignment of multiple point clouds is a basic requirement for an adequate digitization, reconstruction and interpretation of large 3D environments. Due to the recent technological advancements, modern devices are available which allow for simultaneously capturing intensity and range images with high update rates. Hence, such devices can even be used for dynamic scene analysis and for rapid mapping which is particularly required for environmental applications and disaster management, but unfortunately, they also reveal severe restrictions. Facing challenges with respect to noisy range measurements, a limited non-ambiguous range, a limited field of view and the occurrence of scene dynamics, the adequate alignment of captured point clouds has to satisfy additional constraints compared to the classical registration of terrestrial laser scanning (TLS) point clouds for describing static scenes. In this paper, we propose a new methodology for point cloud registration which considers such constraints while maintaining the fundamental properties of high accuracy and low computational effort without relying on a good initial alignment or human interaction. Exploiting 2D image features and 2D/2D correspondences, sparse point clouds of physically almost identical 3D points are derived. Subsequently, these point clouds are aligned with a fast procedure directly taking into account the reliability of the detected correspondences with respect to geometric and radiometric information. The proposed methodology is evaluated and its performance is demonstrated for data captured with a moving sensor platform which has been designed for monitoring from low altitudes. Due to the provided reliability and a fast processing scheme, the proposed methodology offers a high potential for dynamic scene capture and analysis.
PERFORMANCE EVALUATION OF THERMOGRAPHIC CAMERAS FOR PHOTOGRAMMETRIC MEASUREMENTS
Authors: Yastikli N. , Guler E.
Published: May 2013

Abstract. The aim of this research is the performance evaluation of the termographic cameras for possible use for photogrammetric documentation and deformation analyses caused by moisture and isolation problem of the historical and cultural heritage. To perform geometric calibration of the termographic camera, the 3D test object was designed with 77 control points which were distributed in different depths. For performance evaluation, Flir A320 termographic camera with 320 × 240 pixels and lens with 18 mm focal length was used. The Nikon D3X SLR digital camera with 6048 × 4032 pixels and lens with 20 mm focal length was used as reference for comparison. The size of pixel was 25 μm for the Flir A320 termographic camera and 6 μm for the Nikon D3X SLR digital camera. The digital images of the 3D test object were recorded with the Flir A320 termographic camera and Nikon D3X SLR digital camera and the image coordinate of the control points in the images were measured. The geometric calibration parameters, including the focal length, position of principal points, radial and tangential distortions were determined with introduced additional parameters in bundle block adjustments. The measurement of image coordinates and bundle block adjustments with additional parameters were performed using the PHIDIAS digital photogrammetric system. The bundle block adjustment was repeated with determined calibration parameter for both Flir A320 termographic camera and Nikon D3X SLR digital camera. The obtained standard deviation of measured image coordinates was 9.6 μm and 10.5 μm for Flir A320 termographic camera and 8.3 μm and 7.7 μm for Nikon D3X SLR digital camera. The obtained standard deviation of measured image points in Flir A320 termographic camera images almost same accuracy level with digital camera in comparison with 4 times bigger pixel size. The obtained results from this research, the interior geometry of the termographic cameras and lens distortion was modelled efficiently with proposed approach for geometric calibration.
MULTI-SOURCE HIERARCHICAL CONDITIONAL RANDOM FIELD MODEL FOR FEATURE FUSION OF REMOTE SENSING IMAGES AND LIDAR DATA
Authors: Zhang Z. , Yang M. Y. , Zhou M.
Published: May 2013

Abstract. Feature fusion of remote sensing images and LiDAR points cloud data, which have strong complementarity, can effectively play the advantages of multi-class features to provide more reliable information support for the remote sensing applications, such as object classification and recognition. In this paper, we introduce a novel multi-source hierarchical conditional random field (MSHCRF) model to fuse features extracted from remote sensing images and LiDAR data for image classification. Firstly, typical features are selected to obtain the interest regions from multi-source data, then MSHCRF model is constructed to exploit up the features, category compatibility of images and the category consistency of multi-source data based on the regions, and the outputs of the model represents the optimal results of the image classification. Competitive results demonstrate the precision and robustness of the proposed method.
ENHANCED COMPONENT DETECTION ALGORITHM OF FULL-WAVEFORM LIDAR DATA
Authors: Zhou M. , Liu M. H. , Zhang Z. , Wang J. H.
Published: May 2013

Abstract. When full-waveform LiDAR (FW-LiDAR) data are applied to extract the component feature information of interest targets, there exist a problem of components lost during the waveform decomposition procedure, which severely constrains the performance of subsequent targets information extraction. Focusing on the problem above, an enhance component detection algorithm, which combines Finite Mixed Method (FMM), Levenberg-Marquardt (LM) algorithm and Penalized Minimum Matching Distance (PMMD),is proposed in this paper. All of the algorithms for parameters initialization, waveform decomposition and missing component detection have been improved, which greatly increase the precision of component detection, and guarantee the precision of waveform decomposition that could help the weak information extraction of interest targets. The effectiveness of this method is verified by the experimental results of simulation and measured data.
DSMS GENERATION FROM COSMO-SKYMED, RADARSAT-2 AND TERRASAR-X IMAGERY ON BEAUPORT (CANADA) TEST SITE: EVALUATION AND COMPARISON OF DIFFERENT RADARGRAMMETRIC APPROACHES
Authors: Capaldo P. , Crespi M. , Fratarcangeli F. , Nascetti A. , Pieralice F. , Porfiri M. , Toutin T.
Published: May 2013

Abstract. This work is focused on the analysis of potentialities of the radargrammetric DSMs generation using high resolution SAR imagery acquired by three different platforms (COSMO-SkyMed, TerraSAR-X and Radarsat-2) with particular attention to geometric orientation models. Two orientation models have been tested in this work: the rigorous Toutin’s model, developed at the Canada Center for Remote Sensing (CCRS) and implemented in the commercial software package PCI Geomatica, and the radargrammetric model developed at University of Rome La Sapienza and implemented in the scientific software SISAR. A full comparison and analysis has been carried out over Beauport test site (Quebec, Canada), where a LIDAR ground truth and a dense set of GNSS CPs (Check points) are available. Moreover, a preliminary comparison between the DSMs extracted, respectively with SISAR and PCI-Geomatica has been performed. The accuracy of the generated DSMs has been evaluated through the scientific software DEMANAL developed by Prof. K. Jacobsen of University of Hannover. As regards orientation models, the results shown that the Toutin’s model accuracy is slightly better than the SISAR one, even if it is important to underline that the SISAR model is computed without using a priori ground truth information. As concern DSMs assessment, the global DSMs accuracy in term of RMSE is around 4 meter and the two radargrammetric approaches show similar performances.
ROAD REGION DETECTION IN URBAN AREAS COMBINING HIGH-RESOLUTION RGB IMAGE AND LASER SCANNING DATA IN A CLASSIFICATION FRAMEWORK
Authors: Dal Poz A. P. , Mendes T. S. G.
Published: May 2013

Abstract. This paper addresses the problem of road region detection in urban areas using an image classification approach. In order to minimize the spectral superposition of the road (asphalt) class with other classes, the Artificial Neural Networks (ANN) image classification method was used to classify geometrically-integrated high-resolution RGB aerial and laser-derived images. The RGB image was combined with different laser data layers and the ANN classification results showed that the radiometric and geometric laser data allows a better detection of road pixel.
OBJECT-BASED ANALYSIS OF AERIAL PHOTOGRAMMETRIC POINT CLOUD AND SPECTRAL DATA FOR LAND COVER MAPPING
Authors: Debella-Gilo M. , Bjørkelo K. , Breidenbach J. , Rahlf J.
Published: May 2013

Abstract. The acquisition of 3D point data with the use of both aerial laser scanning (ALS) and matching of aerial stereo images coupled with advances in image processing algorithms in the past years provide opportunities to map land cover types with better precision than before. The present study applies Object-Based Image Analysis (OBIA) to 3D point cloud data obtained from matching of stereo aerial images together with spectral data to map land cover types of the Nord-Trøndelag county of Norway. The multi-resolution segmentation algorithm of the Definiens eCognition™ software is used to segment the scenes into homogenous objects. The objects are then classified into different land cover types using rules created based on the definitions given for each land cover type by the Norwegian Forest and Landscape Institute. The quality of the land cover map was evaluated using data collected in the field as part of the Norwegian National Forest Inventory. The results show that the classification has an overall accuracy of about 80% and a kappa index of about 0.65. OBIA is found to be a suitable method for utilizing 3D remote sensing data for land cover mapping in an effort to replace manual delineation methods.
EVALUATION OF PERSISTENT SCATTERER PATTERNS AT BUILDING FACADES BY SIMULATION TECHNIQUES
Authors: Auer S. , Gernhardt S. , Eder K.
Published: May 2013

Abstract. Thanks to the high resolution of modern SAR satellites many persistent scatterers (PS) appear at single buildings, approximately half of them representing building facades. These groups of points often show regular patterns in the SAR images that can be related to repeating structure elements on the facades. Hence, individual buildings can now be monitored over time, either based on amplitude (for change detection) and phase information (using interferometric methods). However, patterns of point signatures are often disrupted or disappear when the imaging geometry is slightly changed. The investigation at hand provides a detailed analysis of PS patterns based on reference data obtained from a photogrammetric survey. The processing of the optical images allows to create a detailed 3D model of the respective facade, which represents the geometrical object information for SAR simulation. From the simulation results the appearance of the point patterns of natural PS in high resolution SAR data can be better understood. The approach and findings for a case study in Munich is described in detail in this paper.
DELINEATION OF BUILDING FOOTPRINTS FROM HIGH RESOLUTION SATELLITE STEREO IMAGERY USING IMAGE MATCHING AND A GIS DATABASE
Authors: Dini G. R. , Jacobsen K. , Heipke C.
Published: May 2013

Abstract. In this paper, a workflow is proposed to delineate building footprints from high resolution satellite stereo images through integration of a Digital Surface Model (DSM), 3D edge matching technique and GIS building polygons. First, Digital Surface Models (DSM) and a normalised DSM are derived by traditional image matching. Three different removal masks are employed to reduce the effects of matching errors, roads and vegetation. We then compare all resulting blobs with the GIS 2D building layer. For blobs without a partner in the database, building outlines are extracted based on 3D edge matching. To do so, edges on epipolar images are first detected individually on each image using the Canny operator. After removing short edges and extracting straight lines, we find the best corresponding lines using various constraints. Finally, the topological relationship of the derived 3D edges is employed to reconstruct the shape of the building and the building footprints using a box-fitting approach. Our experiments on GeoEye-1 stereo images show promising results if buildings are large enough, have simple shape and show good contrast compared to the background.
SUPPORTING UAVS IN LOW VISIBILITY CONDITIONS BY MULTIPLE-PULSE LASER SCANNING DEVICES
Authors: Djuricic A. , Jutzi B.
Published: May 2013

Abstract. Unmanned Aerial Vehicles (UAVs) are nowadays promising platforms for capturing spatial information, because they are low cost solutions, which are easy to bring to the surveying field and can operate automatically. Usually these devices are equipped with visual sensors to support the navigation of the platform or to transmit observations of the environment to the operator. By collecting the data and processing the captured images even an estimation of the observed environment in form of 3D information is available. Therefore Simultaneous Localization and Mapping (SLAM) algorithms are well known for processing data which is captured in the visible domain. However, situations can occur where gathering visual information is difficult due to given limitations in form of low visibility. For example if soft obstacles in form of translucent materials are given in disaster scenarios with smoke and operating has still to be ensured, active optical sensors (e.g. laser scanners) are gaining interest because they can penetrate the soft obstacle and allow to acquire information behind it. A new lightweight (210 g), simplified and minimized scanning unit is now available which allows to capture multiple reflections for each transmitted laser pulse, namely the Hokuyo UTM-30LX-EW. With such a device, it is possible to overcome the above mentioned restrictions or limitations of low visibility by soft obstacles and even measure under critical circumstances. A multi-pulse system can provide accurate measurements on, within, and behind the soft obstacle. This research focuses on investigating the ability and performance of a laser scanner to penetrate the soft obstacle. Thus, investigations on a system that overcomes these limitations and provides a solution will be given. First promising experimental results considering soft obstacle are described.
Near-field wireless sensing of single and multiple open-ended micro coils
Authors: Yousaf A. , Boccard J. M. , Khan F. A. , Reindl L. M.
Published: May 2013

Abstract. In this work we present near-field wireless sensing of single and multiple open-ended micro coils using an electrically small loop antenna. Wirelessly characterized parameters of open-ended micro coils include its resonance frequency, quality factor and inductance. Moreover a wireless frequency-dependent analytical model was developed. Micro coil inductance was extracted from the wirelessly measured signal using a constraint-based least-squares approach. Wireless measurements and analytical fit of micro coils are in strong agreement which validates the analytical model. Finite element method (FEM) simulations of the coupled system were done in COMSOL Multiphysics.
ROBOTRAN: a powerful symbolic gnerator of multibody models
Authors: Docquier N. , Poncelet A. , Fisette P.
Published: May 2013

Abstract. The computational efficiency of symbolic generation was at the root of the emergence of symbolic multibody programs in the eighties. At present, it remains an attractive feature of it since the exponential increase in modern computer performances naturally provides the opportunity to investigate larger systems and more sophisticated models for which real-time computation is a real asset. Nowadays, in the context of mechatronic multibody systems, another interesting feature of the symbolic approach appears when dealing with enlarged multibody models, i.e. including electrical actuators, hydraulic devices, pneumatic suspensions, etc. and requiring specific analyses like control and optimization. Indeed, since symbolic multibody programs clearly distinguish the modeling phase from the analysis process, extracting the symbolic model, as well as some precious ingredients like analytical sensitivities, in order to export it towards any suitable environment (for control or optimization purposes) is quite straightforward. Symbolic multibody model portability is thus very attractive for the analysis of mechatronic applications. In this context, the main features and recent developments of the ROBOTRAN software developed at the Université catholique de Louvain (Belgium) are reviewed in this paper and illustrated via three multibody applications which highlight its capabilities for dealing with very large systems and coping with multiphysics issues.
Influence of regional precipitation patterns on stable isotopes in ice cores from the central Himalayas
Authors: Pang H. , Hou S. , Kaspari S. , Mayewski P. A.
Published: May 2013

Abstract. Several ice cores have been recovered from the Dasuopu Glacier and the East Rongbuk (ER) Glacier in the central Himalayas since the 1990s. Although the distance between the ER and the Dasuopu ice core drilling sites is only ∼125 km, the stable isotopic record (δ18O or δD) of the ER core is interpreted as a precipitation proxy while the Dasuopu core as a temperature proxy. Thus, the climatological significance of the stable isotopic records of these Himalayan ice cores remains a subject of debate. Based on analysis of regional precipitation patterns over the region, we find that the different interpretations of the Dasuopu and Everest isotopic records may not be contradictive. The north–south and west–east seesaws of the Indian Summer Monsoon (ISM) precipitation are primarily responsible for precipitation falling at the ER site, which results in a negative correlation between the ER δ18O or δD record and precipitation amount along the southern slope of the central Himalayas, corresponding to the "amount effect". In addition to the ISM precipitation, non-summer monsoonal precipitation associated with winter westerlies also significantly contributes to precipitation falling at the Dasuopu site, which may cause a positive correlation between the Dasuopu stable isotopic record and temperature, in response to the "temperature effect". Our results have important implications for interpreting the stable isotopic ice core records recovered from different climatological regimes of the Himalayas.
An Integrated Hybrid MCDM Approach for Vendor Selection Problem (Case Study: Iran Khodro)
Authors: Kassaee Massoud , Farrokh Mojtaba , Nia Hassan Hosseini
Published: May 2013

Vendor selection is an important issue in most company based on many criteria that includes ambiguous or uncertain data. Therefore in the study, it is essential that fuzzy approach is employed for coping with the uncertainty and achieving more accurate results. In other hand, the relationships between criteria and sub-criteria are complex; for encompassing the complexity, most conventional decision models cannot help us explain the interrelationships among the criteria. In this paper, a hybrid multi-criteria decision making (MCDM) technique is proposed to determine the structural relationships and the interrelationships among all the evaluation’s dimensions based the Analytic Network Process (ANP) method determining appropriate weightings to each sub-criterion. Then alternatives priority should be determined which can aid the decision making. For the purpose, The TOPSIS (technique for order performance by similarity to idea solution) is used to rank all competing alternatives in terms of their overall performances. In MCDM studies and research, applying TOPSIS in ranking alternatives has recently been customary because of its advantages. In the end, a case study of an Iranian company, in automotive industry, is demonstrated to illustrate the proposed model can improve solving of vendor selection problem.
The Case Study Method in Examining Land Registration Usage
Authors: Barry Michael , Roux Lani
Published: May 2013

The case study method is appropriate for examining how and why landholders choose to use or not use land registration to effect transactions after first registration. The paper covers two related approaches to building theory that explains land registration usage behaviour. The methodologies may be extended to examining usage of other forms of land tenure information system (LTIS). Land titling and registration projects have proliferated in the developing world since World War II, often motivated by the theory that economic benefits will flow from the capital vested in the land that is freed up as a consequence of registration. Many of these projects fail to produce the desired results because the intended beneficiaries do not register transactions after first registration. Surprisingly, few studies investigate the reasons for the failure of land titling projects from the perspective of the intended beneficiaries. The case study method is suited to examine the strategies that landholders use to secure transactions. The advantages of case studies are that they may include all the data the researcher deems relevant, they emphasise the local context, and certain phenomena may be examined more deeply than other methods of inquiry allow. In case studies, researchers can examine issues at the grassroots level while at the same time incorporating the influences of local politics, social change, land administration agency behaviour and the influence of local and national law and policy.
Clinical and Biochemical Outcomes of High-Risk Prostate Cancer Patients treated with Third Generation Prostate Cryosurgery
Authors: Sven Wenske  , Philippa  Cheetham  , Aaron E. Katz
Published: May 2013

Objectives:To report on outcomes after modern-day primary prostate cryosurgery (CS) in D Amico high-risk (PSA >20 ng/ml, Gleason score ¢€°¥8, or tumor stage T2c or T3) localised prostate cancer (PCa) patients treated at a large academic center.
Materials and Methods:730 consecutive cases of total gland prostate CS were reviewed, and 80 men with high-risk disease identified. Clinical data was analyzed, with primary and secondary endpoints being overall survival, cancer-specific survival, biochemical recurrence (BCR), and clinical progression.
Results:Median age was 75.8 (55.4-88.1) years, median presenting PSA 20.0 (2.6-236.5) ng/ml, and median Gleason score 8 (6-10). Median follow-up was 49.6 (8.9-159.3) months. There were three PCa related deaths (4%); 34 (43%) and 39 (49%) men had BCR as defined by the Phoenix- and Stuttgart-criteria, respectively; 24 of the 39 (64%) men were re-biopsied. 13 of 80 (16%) had biopsy proven recurrent PCa. Nine (11%) subsequently underwent salvage CS. Six of the 39 (15%) men with BCR had metastatic disease on bone scan; 19 of 34 (49%) men with BCR received anti-androgen therapy, 18 (95%) of whom had also received neoadjuvant hormonal therapy.
Conclusions:Prostate CS is a controversial treatment for high-risk patients, and our early experience revealed low cancer-specific mortality and morbidity, with encouraging biochemical and local control rates for these high-risk patients. In our series the incidence of metastases was less than that reported by Bolla et al. post-EBRT and hormones, and we therefore believe that prostate CS be strongly considered for these high-risk patients, and mandate that further study of CS for high-risk disease is warranted.
Immunohistochemical Profile and Clinical-Pathological Variants of Breast Cancer in Northeastern Mexico
Authors: Jose Manuel Ornelas-Aguirre  , Laura Maria de la Asunción Perez-Michel
Published: May 2013

Background: Breast cancer is a heterogeneous illness, with subtypes of varying etiology. Estrogen Receptor (ER), Progesterone Receptor (PR) and HER2/neu (Human Epidermal Growth Factor Receptor 2) expressions have been identified as predicting factors.
Objective: To demonstrate the possible association of the five immunohistochemical (IHC) expression profiles with clinical and histopathological variables of breast cancer in northeastern Mexico.
Methodology: In 522 women with breast carcinoma, five IHC profiles were defined [Luminal A, Luminal B, Mixed, HER2/neu and Triple-negative (TN)]. An analysis was done to determine if there were differences between them in relation to the clinical and histopathological variables.
Results: The distribution of the histological subtypes was: luminal A (32.97%), TN (27.53%), HER2/neu (19.02%), mixed (13.41%) and luminal B (7.07%). The average age at diagnosis was 53.07 ± 12.08 years, in 90.5% of the patients the size of the tumor was ≥ 2.0 cm, and 40.94% had lymph node involvement. Luminal A subtype had the highest percentage in the postmenopausal state (63.7%, p=0.071). Illness recurred in 21.01% of the patients (n=116), principally with the TN subtype (28.3%, p=0.012).
Conclusions: This study detected the characterization of IHC subgroups in patients treated for breast cancer at a reference center for cancer treatment in northeastern Mexico.
Elastic Tissue in Nevus Lipomatosus Cutaneous Superficialis
Authors: Ahmed Abdullah Alhumaidi
Published: May 2013

Nevus lipomatosus cutaneous superficialis (NLCS) is a rare benign hamartomatous condition characterized by the presence of mature ectopic adipocytes in the dermis. We studied twelve cases of solitary type of NLCS to demonstrate the status of elastic fibers. All were stained with elastic-van Gieson stain for elastic tissue. Most of the cases showed moderate to marked reduction and thinning of elastic fibers (EF) as compared to adjacent normal dermis. We conclude that thinned and decreased elastic tissue is the cause of NLCS.
A Nonparametric Bayesian Approach to Estimating Malaria Prophylactic Effect After Two Treatments
Authors: Kum Cletus Kwa , Thorburn Daniel , Ghilagaber Gebrenegus , Gil Pedro , Björkman Anders
Published: May 2013

Two treatment regimens for malaria are compared in their abilities to cure and combat reinfection. Bayesian analysis techniques are used to compare two typical treatment therapies for uncomplicated malaria in children under five years, not only in their power to resist recrudescence, but also how long they can postpone recrudescence or reinfection in case of failure. We present a new way of analysing this type of data using Markov Chain Monte Carlo techniques. This is done using data from clinical trials at two different centres. The results which give the full posterior distributions show that artemisinin-based combination therapy is more efficacious than sulfadoxine-pyrimethamine. It both reduced the risk of recrudescence and delayed the time until recrudescence.
A Water‐Soluble Mechanochromic Luminescent Pyrene Derivative Exhibiting Recovery of the Initial Photoluminescence Color in a High‐Humidity Environment
Authors: Sagara Yoshimitsu , Komatsu Toru , Ueno Tasuku , Hanaoka Kenjiro , Kato Takashi , Nagano Tetsuo
Published: May 2013

Switching of the luminescence properties of molecular materials in response to mechanical stimulation is of fundamental interest and also has a range of potential applications. Herein, a water‐soluble mechanochromic luminescent pyrene derivative having two hydrophilic dendrons is reported. This pyrene derivative is the first example of a mechanochromic luminescent organic compound that responds to relative humidity. Mechanical stimulation (grinding) of this pyrene derivative in the solid state results in a change of the photoluminescence from yellow to green. Subsequent exposure to water vapor induces recovery of the initial yellow photoluminescence. The color change is reversible through at least ten cycles. It is also demonstrated that this compound can be applied as a mechano‐sensing material in frictional wear testing for grease, owing to its immiscibility in non‐polar solvents and its non‐crystalline behavior. Transmission electron microscope and atomic force microscope observations of samples prepared from dilute aqueous solutions of the pyrene derivative on suitable substrates, together with dynamic light scattering measurements for the compound in aqueous solution, indicate that this amphiphilic dumbbell‐shaped molecule forms micelles in water.