bluuebunny/crossref_metadata_2025 huggingface dataset:
Accuracy Validation of Large-scale Block Adjustment without Control of ZY3 Images over China
Authors: Yang Bo
Published: August 2016

Mapping from optical satellite images without ground control is one of the goals of photogrammetry. Using 8802 three linear array stereo images (a total of 26406 images) of ZY3 over China, we propose a large-scale and non-control block adjustment method of optical satellite images based on the RPC model, in which a single image is regarded as an adjustment unit to be organized. To overcome the block distortion caused by unstable adjustment without ground control and the excessive accumulation of errors, we use virtual control points created by the initial RPC model of the images as the weighted observations and add them into the adjustment model to refine the adjustment. We use 8000 uniformly distributed high precision check points to evaluate the geometric accuracy of the DOM (Digital Ortho Model) and DSM (Digital Surface Model) production, for which the standard deviations of plane and elevation are 3.6 m and 4.2 m respectively. The geometric accuracy is consistent across the whole block and the mosaic accuracy of neighboring DOM is within a pixel, thus, the seamless mosaic could take place. This method achieves the goal of an accuracy of mapping without ground control better than 5 m for the whole China from ZY3 satellite images.
HIGH PERFORMANCE COMPUTING FOR DSM EXTRACTION FROM ZY-3 TRI-STEREO IMAGERY
Authors: Lu Shuning , Huang Shicun , Pan Zhiqiang , Deng Huawu , Stanley David , Xin Yubin
Published: August 2016

ZY-3 has been acquiring high quality imagery since its launch in 2012 and its tri-stereo (three-view or three-line-array) imagery has become one of the top choices for extracting DSM (Digital Surface Model) products in China over the past few years. The ZY-3 tri-stereo sensors offer users the ability to capture imagery over large regions including an entire territory of a country, such as China, resulting in a large volume of ZY-3 tri-stereo scenes which require timely (e.g., near real time) processing, something that is not currently possible using traditional photogrammetry workstations. This paper presents a high performance computing solution which can efficiently and automatically extract DSM products from ZY-3 tri-stereo imagery. The high performance computing solution leverages certain parallel computing technologies to accelerate computation within an individual scene and then deploys a distributed computing technology to increase the overall data throughput in a robust and efficient manner. By taking advantage of the inherent efficiencies within the high performance computing environment, the DSM extraction process can exploit all combinations offered from a set of tri-stereo images (forward-backword, forward-nadir and backword-nadir). The DSM results merged from all of the potential combinations can minimize blunders (e.g., incorrect matches) and also offer the ability to remove potential occlusions which may exist in a single stereo pair, resulting in improved accuracy and quality versus those that are not merged. Accelerated performance is inherent within each of the individual steps of the DSM extraction workflow, including the collection of ground control points and tie points, image bundle adjustment, the creation of epipolar images, and computing elevations. Preliminary experiments over a large area in China have proven that the high performance computing system can generate high quality and accurate DSM products in a rapid manner.
A METHOD FOR THE POSITIONING AND ORIENTATION OF RAIL-BOUND VEHICLES IN GNSS-FREE ENVIRONMENTS
Authors: Hung R. , King B. A. , Chen W.
Published: August 2016

Mobile Mapping System (MMS) are increasingly applied for spatial data collection to support different fields because of their efficiencies and the levels of detail they can provide. The Position and Orientation System (POS), which is conventionally employed for locating and orienting MMS, allows direct georeferencing of spatial data in real-time. Since the performance of a POS depends on both the Inertial Navigation System (INS) and the Global Navigation Satellite System (GNSS), poor GNSS conditions, such as in long tunnels and underground, introduce the necessity for post-processing. In above-ground railways, mobile mapping technology is employed with high performance sensors for finite usage, which has considerable potential for enhancing railway safety and management in real-time. In contrast, underground railways present a challenge for a conventional POS thus alternative configurations are necessary to maintain data accuracy and alleviate the need for post-processing. This paper introduces a method of rail-bound navigation to replace the role of GNSS for railway applications. The proposed method integrates INS and track alignment data for environment-independent navigation and reduces the demand of post-processing. The principle of rail-bound navigation is presented and its performance is verified by an experiment using a consumer-grade Inertial Measurement Unit (IMU) and a small-scale railway model. The method produced a substantial improvement in position and orientation for a poorly initialised system in centimetre positional accuracy. The potential improvements indicated by, and limitations of rail-bound navigation are also considered for further development in existing railway systems.
MATCHING AERIAL IMAGES TO 3D BUILDING MODELS BASED ON CONTEXT-BASED GEOMETRIC HASHING
Authors: Jung J. , Bang K. , Sohn G. , Armenakis C.
Published: August 2016

In this paper, a new model-to-image framework to automatically align a single airborne image with existing 3D building models using geometric hashing is proposed. As a prerequisite process for various applications such as data fusion, object tracking, change detection and texture mapping, the proposed registration method is used for determining accurate exterior orientation parameters (EOPs) of a single image. This model-to-image matching process consists of three steps: 1) feature extraction, 2) similarity measure and matching, and 3) adjustment of EOPs of a single image. For feature extraction, we proposed two types of matching cues, edged corner points representing the saliency of building corner points with associated edges and contextual relations among the edged corner points within an individual roof. These matching features are extracted from both 3D building and a single airborne image. A set of matched corners are found with given proximity measure through geometric hashing and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on co-linearity equations. The result shows that acceptable accuracy of single image's EOP can be achievable by the proposed registration approach as an alternative to labour-intensive manual registration process.
HYBRID ONLINE MOBILE LASER SCANNER CALIBRATION THROUGH IMAGE ALIGNMENT BY MUTUAL INFORMATION
Authors: Miled Mourad , Soheilian Bahman , Habets Emmanuel , Vallet Bruno
Published: August 2016

This paper proposes an hybrid online calibration method for a laser scanner mounted on a mobile platform also equipped with an imaging system. The method relies on finding the calibration parameters that best align the acquired points cloud to the images. The quality of this intermodal alignment is measured by Mutual information between image luminance and points reflectance. The main advantage and motivation is ensuring pixel accurate alignment of images and point clouds acquired simultaneously, but it is also much more flexible than traditional laser calibration methods.
SATELLITE STEREO BASED DIGITAL SURFACE MODEL GENERATION USING SEMI GLOBAL MATCHING IN OBJECT AND IMAGE SPACE
Authors: Ghuffar S.
Published: August 2016

This paper presents methodology and evaluation of Digital Surface Models (DSM) generated from satellite stereo imagery using Semi Global Matching (SGM) applied in image space and georeferenced voxel space. SGM is a well known algorithm, used widely for DSM generation from airborne and satellite imagery. SGM is typically applied in the image space to compute disparity map corresponding to a stereo image pair. As a different approach, SGM can be applied directly to the georeferenced voxel space similar to the approach of volumetric multi-view reconstruction techniques. The matching in voxel space simplifies the DSM generation pipeline because the stereo rectification and triangulation steps are not required. For a comparison, the complete pipeline for generation of DSM from satellite pushbroom sensors is also presented. The results on the ISPRS satellite stereo benchmark using Worldview stereo imagery of 0.5m resolution shows that the SGM applied in image space produce slightly better results than its object space counterpart. Furthermore, a qualitative analysis of the results on Worldview-3 stereo and Pleiades tri-stereo images are presented.
REFINED SATELLITE IMAGE ORIENTATION IN THE FREE OPEN-SOURCE PHOTOGRAMMETRIC TOOLS APERO/MICMAC
Authors: Rupnik E. , Pierrot Deseilligny M. , Delorme A. , Klinger Y.
Published: August 2016

This publication presents the RPC-based bundle adjustment implemented in the freeware open-source photogrammetric tool Apero/MicMac. The bundle adjustment model is based on some polynomial correction functions, enriched with a physical constraint that introduces the notion of a global sensor rotation into the model. The devised algorithms are evaluated against two datasets consisting of two stereo and a triplet pair of the Pleiades images. Two sets of correction functions and a number of GCPs configurations are examined. The obtained geo-referencing accuracy falls below the size of 1GSD.
HOPC: A NOVEL SIMILARITY METRIC BASED ON GEOMETRIC STRUCTURAL PROPERTIES FOR MULTI-MODAL REMOTE SENSING IMAGE MATCHING
Authors: Ye Yuanxin , Shen Li
Published: August 2016

Automatic matching of multi-modal remote sensing images (e.g., optical, LiDAR, SAR and maps) remains a challenging task in remote sensing image analysis due to significant non-linear radiometric differences between these images. This paper addresses this problem and proposes a novel similarity metric for multi-modal matching using geometric structural properties of images. We first extend the phase congruency model with illumination and contrast invariance, and then use the extended model to build a dense descriptor called the Histogram of Orientated Phase Congruency (HOPC) that captures geometric structure or shape features of images. Finally, HOPC is integrated as the similarity metric to detect tie-points between images by designing a fast template matching scheme. This novel metric aims to represent geometric structural similarities between multi-modal remote sensing datasets and is robust against significant non-linear radiometric changes. HOPC has been evaluated with a variety of multi-modal images including optical, LiDAR, SAR and map data. Experimental results show its superiority to the recent state-of-the-art similarity metrics (e.g., NCC, MI, etc.), and demonstrate its improved matching performance.
A NEW ONTOLOGICAL PERSPECTIVE FOR INTEGRATION OF SOCIAL AND PHYSICAL ENVIRONMENTS: DISABILITY AND REHABILITATION CONTEXT
Authors: Gharebaghi Amin , Mostafavi Mir Abolfazl
Published: August 2016

Social dimension of environment is an important aspect that should be reflected in research works related to studying the interactions between human and the environment. However, this dimension is usually neglected when representing the environment in geographic information systems for different applications. For instance, disability as a result of the interaction between human and environment is influenced by social and physical dimensions of environment. Although, this aspect is highlighted in most conceptual disability models by defining various taxonomies of the environment, from ontological perspective justifying and connecting social dimension to the physical dimension of the environment is not clearly determined. Integrating social dimension of the environment with its physical dimension for disability studies is a challenging task, which is the main objective of the present study. Here, we review some of the disability models and their perspective about classifying the environment. Then, from ontological perspective, their limitations are discussed and a new approach for the classification of concepts form the environment is presented. This approach facilitates and simplifies integration of social dimension in ontologies for more effective assessment of disability issue in Geographic Information System.
AUTOMATED PHOTOGRAMMETRIC IMAGE MATCHING WITH SIFT ALGORITHM AND DELAUNAY TRIANGULATION
Authors: Karagiannis Georgios , Antón Castro Francesc , Mioc Darka
Published: August 2016

An algorithm for image matching of multi-sensor and multi-temporal satellite images is developed. The method is based on the SIFT feature detector proposed by Lowe in (Lowe, 1999). First, SIFT feature points are detected independently in two images (reference and sensed image). The features detected are invariant to image rotations, translations, scaling and also to changes in illumination, brightness and 3-dimensional viewpoint. Afterwards, each feature of the reference image is matched with one in the sensed image if, and only if, the distance between them multiplied by a threshold is shorter than the distances between the point and all the other points in the sensed image. Then, the matched features are used to compute the parameters of the homography that transforms the coordinate system of the sensed image to the coordinate system of the reference image. The Delaunay triangulations of each feature set for each image are computed. The isomorphism of the Delaunay triangulations is determined to guarantee the quality of the image matching. The algorithm is implemented in Matlab and tested on World-View 2, SPOT6 and TerraSAR-X image patches.
A RELIABILITY EVALUATION SYSTEM OF ASSOCIATION RULES
Authors: Chen Jiangping , Feng Wanshu , Luo Minghai
Published: August 2016

In mining association rules, the evaluation of the rules is a highly important work because it directly affects the usability and applicability of the output results of mining. In this paper, the concept of reliability was imported into the association rule evaluation. The reliability of association rules was defined as the accordance degree that reflects the rules of the mining data set. Such degree contains three levels of measurement, namely, accuracy, completeness, and consistency of rules. To show its effectiveness, the "accuracy-completeness-consistency" reliability evaluation system was applied to two extremely different data sets, namely, a basket simulation data set and a multi-source lightning data fusion. Results show that the reliability evaluation system works well in both simulation data set and the actual problem. The three-dimensional reliability evaluation can effectively detect the useless rules to be screened out and add the missing rules thereby improving the reliability of mining results. Furthermore, the proposed reliability evaluation system is applicable to many research fields; using the system in the analysis can facilitate obtainment of more accurate, complete, and consistent association rules.
DEVELOPMENT OF MATCHED (MIGRATORY ANALYTICAL TIME CHANGE EASY DETECTION) METHOD FOR SATELLITE-TRACKED MIGRATORY BIRDS
Authors: Doko Tomoko , Chen Wenbo , Higuchi Hiroyoshi
Published: August 2016

Satellite tracking technology has been used to reveal the migration patterns and flyways of migratory birds. In general, bird migration can be classified according to migration status. These statuses include the wintering period, spring migration, breeding period, and autumn migration. To determine the migration status, periods of these statuses should be individually determined, but there is no objective method to define 'a threshold date' for when an individual bird changes its status. The research objective is to develop an effective and objective method to determine threshold dates of migration status based on satellite-tracked data. The developed method was named the “MATCHED (Migratory Analytical Time Change Easy Detection) method”. In order to demonstrate the method, data acquired from satellite-tracked Tundra Swans were used. MATCHED method is composed by six steps: 1) dataset preparation, 2) time frame creation, 3) automatic identification, 4) visualization of change points, 5) interpretation, and 6) manual correction. Accuracy was tested. In general, MATCHED method was proved powerful to identify the change points between migration status as well as stopovers. Nevertheless, identifying “exact” threshold dates is still challenging. Limitation and application of this method was discussed.
INVARIANT DESCRIPTOR LEARNING USING A SIAMESE CONVOLUTIONAL NEURAL NETWORK
Authors: Chen L. , Rottensteiner F. , Heipke C.
Published: August 2016

In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.
PHOTOGRAMMETRIC ACCURACY AND MODELING OF ROLLING SHUTTER CAMERAS
Authors: Ye W. , Qiao G. , Kong F. , Guo S. , Ma X. , Tong X. , Li R.
Published: August 2016

Global climate change is one of the major challenges that all nations are commonly facing. Long-term observations of the Antarctic ice sheet have been playing a critical role in quantitatively estimating and predicting effects resulting from the global changes. The film-based ARGON reconnaissance imagery provides a remarkable data source for studying the Antarctic ice-sheet in 1960s, thus greatly extending the time period of Antarctica surface observations. To deal with the low-quality images and the unavailability of camera poses, a systematic photogrammetric approach is proposed to reconstruct the interior and exterior orientation information for further glacial mapping applications, including ice flow velocity mapping and mass balance estimation. Some noteworthy details while performing geometric modelling using the ARGON images were introduced, including methods and results for handling specific effects of film deformation, damaged or missing fiducial marks and calibration report, automatic fiducial mark detection, control point selection through Antarctic shadow and ice surface terrain analysis, and others. Several sites in East Antarctica were tested. As an example, four images in the Byrd glacier region were used to assess the accuracy of the geometric modelling. A digital elevation model (DEM) and an orthophoto map of Byrd glacier were generated. The accuracy of the ground positions estimated by using independent check points is within one nominal pixel of 140 m of ARGON imagery. Furthermore, a number of significant features, such as ice flow velocity and regional change patterns, will be extracted and analysed.
URBAN TREE CLASSIFICATION USING FULL-WAVEFORM AIRBORNE LASER SCANNING
Authors: Koma Zs. , Koenig K. , Höfle B.
Published: August 2016

Vegetation mapping in urban environments plays an important role in biological research and urban management. Airborne laser scanning provides detailed 3D geodata, which allows to classify single trees into different taxa. Until now, research dealing with tree classification focused on forest environments. This study investigates the object-based classification of urban trees at taxonomic family level, using full-waveform airborne laser scanning data captured in the city centre of Vienna (Austria). The data set is characterised by a variety of taxa, including deciduous trees (beeches, mallows, plane trees and soapberries) and the coniferous pine species. A workflow for tree object classification is presented using geometric and radiometric features. The derived features are related to point density, crown shape and radiometric characteristics. For the derivation of crown features, a prior detection of the crown base is performed. The effects of interfering objects (e.g. fences and cars which are typical in urban areas) on the feature characteristics and the subsequent classification accuracy are investigated. The applicability of the features is evaluated by Random Forest classification and exploratory analysis. The most reliable classification is achieved by using the combination of geometric and radiometric features, resulting in 87.5% overall accuracy. By using radiometric features only, a reliable classification with accuracy of 86.3% can be achieved. The influence of interfering objects on feature characteristics is identified, in particular for the radiometric features. The results indicate the potential of using radiometric features in urban tree classification and show its limitations due to anthropogenic influences at the same time.
PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION
Authors: Lu Xiaohu , Yao Jian , Tu Jinge , Li Kai , Li Li , Liu Yahui
Published: August 2016

In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slicemerging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.
AUTOMATIC EXTRACTION OF DTM FROM LOW RESOLUTION DSM BY TWOSTEPS SEMI-GLOBAL FILTERING
Authors: Zhang Yanfeng , Zhang Yongjun , Zhang Yi , Li Xin
Published: August 2016

Automatically extracting DTM from DSM or LiDAR data by distinguishing non-ground points from ground points is an important issue. Many algorithms for this issue are developed, however, most of them are targeted at processing dense LiDAR data, and lack the ability of getting DTM from low resolution DSM. This is caused by the decrease of distinction on elevation variation between steep terrains and surface objects. In this paper, a method called two-steps semi-global filtering (TSGF) is proposed to extract DTM from low resolution DSM. Firstly, the DSM slope map is calculated and smoothed by SGF (semi-global filtering), which is then binarized and used as the mask of flat terrains. Secondly, the DSM is segmented with the restriction of the flat terrains mask. Lastly, each segment is filtered with semi-global algorithm in order to remove non-ground points, which will produce the final DTM. The first SGF is based on global distribution characteristic of large slope, which distinguishes steep terrains and flat terrains. The second SGF is used to filter non-ground points on DSM within flat terrain segments. Therefore, by two steps SGF non-ground points are removed robustly, while shape of steep terrains is kept. Experiments on DSM generated by ZY3 imagery with resolution of 10-30m demonstrate the effectiveness of the proposed method.
RECTIFICATION AND ROBUST MATCHING USING ORIENTED IMAGE TRIPLETS FOR MINIMALLY INVASIVE SURGERY
Authors: Conen N. , Jepping C. , Luhmann T. , Maas H.-G.
Published: August 2016

Stereo endoscopes for minimally invasive surgery have been available on the market for several years and are well established in some areas. In practice, they offer a stereoscopic view to the surgeon but are not yet intended for 3D measurements. However, using current knowledge about the camera system and the difficult conditions in object space, it is possible to reconstruct a highly accurate surface model of the current endoscopic view. In particular for medical interventions, a highly reliable point cloud and real-time computation are required. To obtain good reliability, a miniaturised trinocular camera system is introduced that reduces the amount of outliers. To reduce computation time, an approach for generation of rectified image triplets and their corresponding interior and exterior camera parameters has been developed. With these modified and parameterised images it is possible to directly process 3D measurements in object space. Accordingly, an efficient semi-global optimisation is implemented by the authors. In this paper the special camera system, the rectification approach and the applied methodology of matching in rectified image triplets are explained. Finally, first results are presented. In conclusion, the trinocular camera system provides more reliable point clouds than a binocular one, especially for areas with repetitive or poor texture. Currently, the benefit of the third camera is not as great as desired.
A GAUSSIAN PROCESS BASED MULTI-PERSON INTERACTION MODEL
Authors: Klinger T. , Rottensteiner F. , Heipke C.
Published: August 2016

Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all interrelated persons. As shown by the experiments, the model is capable of yielding more plausible predictions even in the presence of mutual occlusions or missing measurements. The approach is evaluated on a publicly available benchmark and outperforms other state-of-the-art trackers.
STRUCTURELESS BUNDLE ADJUSTMENT WITH SELF-CALIBRATION USING ACCUMULATED CONSTRAINTS
Authors: Cefalu A. , Haala N. , Fritsch D.
Published: August 2016

Bundle adjustment based on collinearity is the most widely used optimization method within image based scene reconstruction. It incorporates observed image coordinates, exterior and intrinsic camera parameters as well as object space coordinates of the observed points. The latter dominate the resulting nonlinear system, in terms of the number of unknowns which need to be estimated. In order to reduce the size of the problem regarding memory footprint and computational effort, several approaches have been developed to make the process more efficient, e.g. by exploitation of sparsity or hierarchical subdivision. Some recent developments express the bundle problem through epipolar geometry and scale consistency constraints which are free of object space coordinates. These approaches are usually referred to as structureless bundle adjustment. The number of unknowns in the resulting system is drastically reduced. However, most work in this field is focused on optimization towards speed and considers calibrated cameras, only. We present our work on structureless bundle adjustment, focusing on precision issues as camera calibration and residual weighting. We further investigate accumulation of constraint residuals as an approach to decrease the number of rows of the Jacobian matrix.
FAST PROBABILISTIC FUSION OF 3D POINT CLOUDS VIA OCCUPANCY GRIDS FOR SCENE CLASSIFICATION
Authors: Kuhn Andreas , Huang Hai , Drauschke Martin , Mayer Helmut
Published: August 2016

High resolution consumer cameras on Unmanned Aerial Vehicles (UAVs) allow for cheap acquisition of highly detailed images, e.g., of urban regions. Via image registration by means of Structure from Motion (SfM) and Multi View Stereo (MVS) the automatic generation of huge amounts of 3D points with a relative accuracy in the centimeter range is possible. Applications such as semantic classification have a need for accurate 3D point clouds, but do not benefit from an extremely high resolution/density. In this paper, we, therefore, propose a fast fusion of high resolution 3D point clouds based on occupancy grids. The result is used for semantic classification. In contrast to state-of-the-art classification methods, we accept a certain percentage of outliers, arguing that they can be considered in the classification process when a per point belief is determined in the fusion process. To this end, we employ an octree-based fusion which allows for the derivation of outlier probabilities. The probabilities give a belief for every 3D point, which is essential for the semantic classification to consider measurement noise. For an example point cloud with half a billion 3D points (cf. Figure 1), we show that our method can reduce runtime as well as improve classification accuracy and offers high scalability for large datasets.
OBJECT-BASED COREGISTRATION OF TERRESTRIAL PHOTOGRAMMETRIC AND ALS POINT CLOUDS IN FORESTED AREAS
Authors: Polewski P. , Erickson A. , Yao W. , Coops N. , Krzystek P. , Stilla U.
Published: August 2016

Airborne Laser Scanning (ALS) and terrestrial photogrammetry are methods applicable for mapping forested environments. While ground-based techniques provide valuable information about the forest understory, the measured point clouds are normally expressed in a local coordinate system, whose transformation into a georeferenced system requires additional effort. In contrast, ALS point clouds are usually georeferenced, yet the point density near the ground may be poor under dense overstory conditions. In this work, we propose to combine the strengths of the two data sources by co-registering the respective point clouds, thus enriching the georeferenced ALS point cloud with detailed understory information in a fully automatic manner. Due to markedly different sensor characteristics, coregistration methods which expect a high geometric similarity between keypoints are not suitable in this setting. Instead, our method focuses on the object (tree stem) level. We first calculate approximate stem positions in the terrestrial and ALS point clouds and construct, for each stem, a descriptor which quantifies the 2D and vertical distances to other stem centers (at ground height). Then, the similarities between all descriptor pairs from the two point clouds are calculated, and standard graph maximum matching techniques are employed to compute corresponding stem pairs (tiepoints). Finally, the tiepoint subset yielding the optimal rigid transformation between the terrestrial and ALS coordinate systems is determined. We test our method on simulated tree positions and a plot situated in the northern interior of the Coast Range in western Oregon, USA, using ALS data (76 x 121 m2) and a photogrammetric point cloud (33 x 35 m2) derived from terrestrial photographs taken with a handheld camera. Results on both simulated and real data show that the proposed stem descriptors are discriminative enough to derive good correspondences. Specifically, for the real plot data, 24 corresponding stems were coregistered with an average 2D position deviation of 66 cm.
OPENWEBGLOBE 2: VISUALIZATION OF COMPLEX 3D-GEODATA IN THE (MOBILE) WEBBROWSER
Authors: Christen M.
Published: August 2016

Providing worldwide high resolution data for virtual globes consists of compute and storage intense tasks for processing data. Furthermore, rendering complex 3D-Geodata, such as 3D-City models with an extremely high polygon count and a vast amount of textures at interactive framerates is still a very challenging task, especially on mobile devices. This paper presents an approach for processing, caching and serving massive geospatial data in a cloud-based environment for large scale, out-of-core, highly scalable 3D scene rendering on a web based virtual globe. Cloud computing is used for processing large amounts of geospatial data and also for providing 2D and 3D map data to a large amount of (mobile) web clients. In this paper the approach for processing, rendering and caching very large datasets in the currently developed virtual globe “OpenWebGlobe 2” is shown, which displays 3D-Geodata on nearly every device.
VEHICLE DETECTION OF AERIAL IMAGE USING TV-L1 TEXTURE DECOMPOSITION
Authors: Wang Y. , Wang G. , Li Y. , Huang Y.
Published: August 2016

Vehicle detection from high-resolution aerial image facilitates the study of the public traveling behavior on a large scale. In the context of road, a simple and effective algorithm is proposed to extract the texture-salient vehicle among the pavement surface. Texturally speaking, the majority of pavement surface changes a little except for the neighborhood of vehicles and edges. Within a certain distance away from the given vector of the road network, the aerial image is decomposed into a smoothly-varying cartoon part and an oscillatory details of textural part. The variational model of Total Variation regularization term and L1 fidelity term (TV-L1) is adopted to obtain the salient texture of vehicles and the cartoon surface of pavement. To eliminate the noise of texture decomposition, regions of pavement surface are refined by seed growing and morphological operation. Based on the shape saliency analysis of the central objects in those regions, vehicles are detected as the objects of rectangular shape saliency. The proposed algorithm is tested with a diverse set of aerial images that are acquired at various resolution and scenarios around China. Experimental results demonstrate that the proposed algorithm can detect vehicles at the rate of 71.5% and the false alarm rate of 21.5%, and that the speed is 39.13 seconds for a 4656 x 3496 aerial image. It is promising for large-scale transportation management and planning.
TOWARDS COMPLETE, GEO-REFERENCED 3D MODELS FROM CROWD-SOURCED AMATEUR IMAGES
Authors: Hartmann W. , Havlena M. , Schindler K.
Published: August 2016

Despite a lot of recent research, photogrammetric reconstruction from crowd-sourced imagery is plagued by a number of recurrent problems. (i) The resulting models are chronically incomplete, because even touristic landmarks are photographed mostly from a few “canonical” viewpoints. (ii) Man-made constructions tend to exhibit repetitive structure and rotational symmetries, which lead to gross errors in the 3D reconstruction and aggravate the problem of incomplete reconstruction. (iii) The models are normally not geo-referenced. In this paper, we investigate the possibility of using sparse GNSS geo-tags from digital cameras to address these issues and push the boundaries of crowd-sourced photogrammetry. A small proportion of the images in Internet collections (≈ 10 %) do possess geo-tags. While the individual geo-tags are very inaccurate, they nevertheless can help to address the problems above. By providing approximate geo-reference for partial reconstructions they make it possible to fuse those pieces into more complete models; the capability to fuse partial reconstruction opens up the possibility to be more restrictive in the matching phase and avoid errors due to repetitive structure; and collectively, the redundant set of low-quality geo-tags can provide reasonably accurate absolute geo-reference. We show that even few, noisy geo-tags can help to improve architectural models, compared to puristic structure-from-motion only based on image correspondence.
IMAGE-GUIDED NON-LOCAL DENSE MATCHING WITH THREE-STEPS OPTIMIZATION
Authors: Huang Xu , Zhang Yongjun , Yue Zhaoxi
Published: August 2016

This paper introduces a new image-guided non-local dense matching algorithm that focuses on how to solve the following problems: 1) mitigating the influence of vertical parallax to the cost computation in stereo pairs; 2) guaranteeing the performance of dense matching in homogeneous intensity regions with significant disparity changes; 3) limiting the inaccurate cost propagated from depth discontinuity regions; 4) guaranteeing that the path between two pixels in the same region is connected; and 5) defining the cost propagation function between the reliable pixel and the unreliable pixel during disparity interpolation. This paper combines the Census histogram and an improved histogram of oriented gradient (HOG) feature together as the cost metrics, which are then aggregated based on a new iterative non-local matching method and the semi-global matching method. Finally, new rules of cost propagation between the valid pixels and the invalid pixels are defined to improve the disparity interpolation results. The results of our experiments using the benchmarks and the Toronto aerial images from the International Society for Photogrammetry and Remote Sensing (ISPRS) show that the proposed new method can outperform most of the current state-of-the-art stereo dense matching methods.
THE EFFECT OF ILLUMINATION ON STEREO DTM QUALITY: SIMULATIONS IN SUPPORT OF EUROPA EXPLORATION
Authors: Kirk R. L. , Howington-Kraus E. , Hare T. M. , Jorda L.
Published: August 2016

We have investigated how the quality of stereoscopically measured topography degrades with varying illumination, in particular the ranges of incidence angles and illumination differences over which useful digital topographic models (DTMs) can be recovered. Our approach is to make high-fidelity simulated image pairs of known topography and compare DTMs from stereoanalysis of these images with the input data. Well-known rules of thumb for horizontal resolution (>3–5 pixels) and matching precision (~0.2–0.3 pixels) are generally confirmed, but the best achievable resolution at high incidence angles is ~15 pixels, probably as a result of smoothing internal to the matching algorithm. Single-pass stereo imaging of Europa is likely to yield DTMs of consistent (optimal) quality for all incidence angles ≤85°, and certainly for incidence angles between 40° and 85°. Simulations with pairs of images in which the illumination is not consistent support the utility of shadow tip distance (STD) as a measure of illumination difference, but also suggest new and simpler criteria for evaluating the suitability of stereopairs based on illumination geometry. Our study was motivated by the needs of a mission to Europa, but the approach and (to first order) the results described here are relevant to a wide range of planetary investigations.
EXTENSION AND EVALUATION OF THE AGAST FEATURE DETECTOR
Authors: Zhang Hongmou , Wohlfeil Jürgen , Grießbach Denis
Published: August 2016

Vision-aided inertial navigation is a navigation method which combines inertial navigation with computer vision techniques. It can provide a six degrees of freedom navigation solution from passive measurements without external referencing (e.g. GPS). Thus, it can operate in unknown environments without any prior knowledge. Such a system, called IPS (Integrated Positioning System) is developed by the German Aerospace Center (DLR).

 For optical navigation applications, a reliable and efficient feature detector is a crucial component. With the publication of AGAST, a new feature detector has been presented, which is faster than other feature detectors. To apply AGAST to optical navigation applications, we propose several methods to improve its performance. Based on a new non-maximum suppression algorithm, automatic threshold adaption algorithm in combination with an image split method, the optimized AGAST provides higher reliability and efficiency than the original implementation using the Kanade Lucas Tomasi (KLT) feature detector. Finally, we compare the performance of the optimized AGAST with the KLT feature detector in the context of IPS. The presented approach is tested using real data from typical indoor scenes, evaluated on the accuracy of the navigation solution. The comparison demonstrates a significant performance improvement achieved by the optimized AGAST.
COMPARATIVE ANALYSIS OF GLOBAL DIGITAL ELEVATION MODELS AND ULTRA-PROMINENT MOUNTAIN PEAKS
Authors: Grohmann Carlos H.
Published: August 2016

Global Digital Elevation Models (GDEMs) are datasets of vital importance for regional-scale analysis in areas such as geomorphology, [paleo]climatology, oceanography and biodiversity. In this work I present a comparative assessment of the datasets ETOPO1 (1’ resolution), GTOPO30, GLOBE, SRTM30 PLUS, GMTED2010 and ACE2 (30”) against the altitude of the world’s ultra prominent peaks. GDEMs’ elevations show an expected tendency of underestimating the peak’s altitude, but differences reach 3,500 m. None of the GDEMs captures the full range of elevation on Earth and they do not represent well the altitude of the most prominent peaks. Some of these problems could be addressed with the release of NASADEM, but the smoothing effect caused by moving-window resampling can only be tackled by using new techniques, such as scale-adaptative kernels and curvature-based terrain generalisation.
ENHANCED DATA DISCOVERABILITY FOR IN SITU HYPERSPECTRAL DATASETS
Authors: Rasaiah B. , Bellman C. , Hewson R.D. , Jones S. D. , Malthus T. J.
Published: August 2016

Field spectroscopic metadata is a central component in the quality assurance, reliability, and discoverability of hyperspectral data and the products derived from it. Cataloguing, mining, and interoperability of these datasets rely upon the robustness of metadata protocols for field spectroscopy, and on the software architecture to support the exchange of these datasets. Currently no standard for in situ spectroscopy data or metadata protocols exist. This inhibits the effective sharing of growing volumes of in situ spectroscopy datasets, to exploit the benefits of integrating with the evolving range of data sharing platforms. A core metadataset for field spectroscopy was introduced by Rasaiah et al., (2011-2015) with extended support for specific applications. This paper presents a prototype model for an OGC and ISO compliant platform-independent metadata discovery service aligned to the specific requirements of field spectroscopy. In this study, a proof-of-concept metadata catalogue has been described and deployed in a cloud-based architecture as a demonstration of an operationalized field spectroscopy metadata standard and web-based discovery service.
DEFINING EARTH DATA BATCH PROCESSING TASKS BY MEANS OF A FLEXIBLE WORKFLOWDESCRIPTION LANGUAGE
Authors: Nandra Constantin , Gorgan Dorian
Published: August 2016

This paper aims to present some of the main features of the Workflow Description Language (WorDeL) and demonstrate their usage in defining Earth Data processing tasks. This description language is based on the flexible description of processing tasks as workflows, composed of basic processing operators. This approach allows the language to offer an intuitive way of representing processing tasks, without requiring programming expertise from its users. It also allows its users to employ and integrate existing functionality into their design, thereby reducing the design complexity and development effort of newly defined processing workflows. WorDeL supports the transparent adaptive parallelization of the processing tasks over high performance computation architectures, such as cloud-based solutions. Throughout the paper, we will exemplify this language’s use in creating flexible, reusable and easy-to-understand earth data processing descriptions, with an emphasis on satellite image processing.
FIRST EXPERIMENTS WITH THE TANGO TABLET FOR INDOOR SCANNING
Authors: Diakité Abdoulaye A. , Zlatanova Sisi
Published: August 2016

During the last two decades, the third dimension took an important place in the heart of every multimedia. While the 3D technologies mainly used to be tools and subject for researchers, they are becoming commercially available to large public. To make it even more accessible, the Project Tango, leaded by Google, integrates in a simple Android tablet sensors that are able to perform acquisition of the 3D information of a real life scene. This makes it possible for a large number of applications to have access to it, ranging from gaming to indoor navigation, including virtual and augmented reality. In this paper we investigate the ability of the Tango tablet to perform the acquisition of indoor building environment to support application such as indoor navigation. We proceed to several scans in different buildings and we study the characteristics of the output models.
POSITION, LOCATION, PLACE AND AREA: AN INDOOR PERSPECTIVE
Authors: Sithole George , Zlatanova Sisi
Published: August 2016

Over the last decade, harnessing the commercial potential of smart mobile devices in indoor environments has spurred interest in indoor mapping and navigation. Users experience indoor environments differently. For this reason navigational models have to be designed to adapt to a user’s personality, and to reflect as many cognitive maps as possible. This paper presents an extension of a previously proposed framework. In this extension the notion of placement is accounted for, thereby enabling one aspect of the ‘personalised indoor experience’. In the paper, firstly referential expressions are used as a tool to discuss the different ways of thinking of placement within indoor spaces. Next, placement is expressed in terms of the concept of Position, Location, Place and Area. Finally, the previously proposed framework is extended to include these concepts of placement. An example is provided of the use of the extended framework.

 Notable characteristics of the framework are: (1) Sub-spaces, resources and agents can simultaneously possess different types of placement, e.g., a person in a room can have an xyz position and a location defined by the room number. While these entities can simultaneously have different forms of placement, only one is dominant. (2) Sub-spaces, resources and agents are capable of possessing modifiers that alter their access and usage. (3) Sub-spaces inherit the modifiers of the resources or agents contained in them. (4) Unlike conventional navigational models which treat resources and obstacles as different types of entities, in the proposed framework there are only resources and whether a resource is an obstacle is determined by a modifier that determines whether a user can access the resource. The power of the framework is that it blends the geometry and topology of space, the influence of human activity within sub-spaces together with the different notions of placement in a way that is simple and yet very flexible.