Enhancment of dense urban digital surface models from VHR optical satellite stereo data by pre-segmentation and object detection

The generation of digital surface models (DSM) of urban areas from very high resolution (VHR) stereo satellite imagery requires advanced methods. In the classical approach of DSM generation from stereo satellite imagery, interest points are extracted and correlated between the stereo mates using an area based matching followed by a least-squares sub-pixel refinement step. After a region growing the 3D point list is triangulated to the resulting DSM. In urban areas this approach fails due to the size of the correlation window, which smoothes out the usual steep edges of buildings. Also missing correlations as for partly – in one or both of the images – occluded areas will simply be interpolated in the triangulation step. So an urban DSM generated with the classical approach results in a very smooth DSM with missing steep walls, narrow streets and courtyards. To overcome these problems algorithms from computer vision are introduced and adopted to satellite imagery. These algorithms do not work using local optimisation like the area-based matching but try to optimize a (semi-)global cost function. Analysis shows that dynamic programming approaches based on epipolar images like dynamic line warping or semiglobal matching yield the best results according to accuracy and processing time. These algorithms can also detect occlusions – areas not visible in one or both of the stereo images. Beside these also the time and memory consuming step of handling and triangulating large point lists can be omitted due to the direct operation on epipolar images and direct generation of a so called disparity image fitting exactly on the first of the stereo images. This disparity image – representing already a sort of a dense DSM – contains the distances measured in pixels in the epipolar direction (or a no-data value for a detected occlusion) for each pixel in the image. Despite the global optimization of the cost function many outliers, mismatches and erroneously detected occlusions remain, especially if only one stereo pair is available. To enhance these dense DSM – the disparity image – a pre-segmentation approach is presented in this paper. Since the disparity image is fitting exactly on the first of the two stereo partners (beforehand transformed to epipolar geometry) a direct correlation between image pixels and derived heights (the disparities) exist. This feature of the disparity image is exploited to integrate additional knowledge from the image into the DSM. This is done by segmenting the stereo image, transferring the segmentation information to the DSM and performing a statistical analysis on each of the created DSM segments. Based on this analysis and spectral information a coarse object detection and classification can be performed and in turn the DSM can be enhanced. After the description of the proposed method some results are shown and discussed

Метод обнаружения нескольких сферических объектов в пространстве

В данной работе продолжены исследования, посвященные решению задачи поиска нескольких сферических поверхностей по заданному трехмерному массиву точек в пространстве. Предложен подход, позволяющий локализовать в пространстве такие объекты и вычислить их параметры. Рассмотрены пути сокращения вычислительной сложности предложенного алгоритмаIn this work the researches devoted to the decision of the task of search of several spherical surfaces in space, the points set by a three-dimensional array are continued. The approach allowing to localize in space such objects and to calculate their parameters is offered. Explicitly computing complexity of the offered algorithm is researche

Application of Hough transform to the recognitions problem of regular solutions of dynamical systems

In this work the investigations of the recognitions problem of regular solutions of autonomous dynamical systems are continued. As shown in [1], this problem reduces to the recognition of three-dimensional convex closed analytic curves constructed on the Poisson sphere by means the Poincaré sections [2], [3]. In some cases, these curves are circles which lie on the surface of sphere. For recognition of such curves in this paper a new algorithm was formulated. He is extending the Circle Hough Transform to three-dimensional case and is called a Generalized Circle Hough Transform. The computational complexity of this algorithm can be reduced to the computational complexity in the two-dimensional case.У цій роботі продовжуються дослідження задачі розпізнавання регулярних розв’язків автономних динамічних систем. Як показано в [1], ця задача зводиться до розпізнавання тривимірних опуклих замкнутих аналітичних кривих, побудованих на сфері Пуассона за допомогою перетину Пуанкаре [2], [3]. У деяких випадках ці криві є колами, які лежать на сфері. Для розпізнання таких кривих у цій роботі пропонується новий алгоритм. Запропонований метод узагальнює класичне перетворення Хафа на тривимірний випадок і називається сферичне узагальнене перетворення Хафа. У роботі показано, як обчислювальну складність цього алгоритму звести до обчислювальної складності двовимірного випадку.В этой работе продолжаются исследования задачи распознавания регулярных решений автономных динамических систем. Как показано в [1], эта задача сводится к распознаванию трехмерных выпуклых замкнутых аналитических кривых, построенных на сфере Пуассона с помощью сечения Пуанкаре [2], [3]. В некоторых случаях эти кривые являются окружностями, которые лежат на сфере. Для распознания таких кривых в этой работе предлагается новый алгоритм. Предложенный метод обобщает классическое преобразование Хафа на трехмерный случай и называется сферическое обобщенное преобразование Хафа. В работе показано, как вычислительную сложность этого алгоритма свести к вычислительной сложности двумерного случая

Updating urban cadastral cartography using LiDAR and GIS

[EN] This paper presents a methodology to automatically update urban cartography using LiDAR (Light Detección And Ranging) data and GIS (Geographic Information Systems). It is worth having access to large scale urban cartography either for the private sector or for the general public; therefore, periodic map updating is requested by local authorities. However, as it is difficult to detect new buildings on outdated plans, most of the times a full update is carried out without considering the related costs of this action. This paper introduces a method to update cartography after detecting changes in LiDAR data. In addition, several algorithms are presented to transform 2D cartography in 3D, making use of a least squares adjustment of the height information delivered by the LiDAR technology.[ES] Este artículo presenta una metodología para actualizar cartografía urbana de forma automática utilizando datos LiDAR (Light Detección And Ranging) y SIG (Sistema de Información Geográfica). La gran utilidad de disponer de cartografía urbana a grandes escalas tanto para el sector privado como público motiva la actualización periódica de la misma por parte de las administraciones locales. No obstante, dada la dificultad de detectar nuevas edificaciones en la cartografía obsoleta, en la mayoría de los casos se opta por su renovación completa con los costes asociados que ello conlleva. El presente artículo muestra una metodología para actualizar cartografía, previa detección de cambios utilizando datos LiDAR. Además, se exponen distintos algoritmos para transformar cartografía 2D a 3D a partir de un ajuste mínimo cuadrático utilizando la información altimétrica que proporciona la tecnología LiDAR.Sánchez Lopera, J.; Lerma García, JL. (2012). Actualización de cartografía catastral urbana mediante LiDAR y SIG. GeoFocus. 12:53-70. http://hdl.handle.net/10251/4364453701

Automated Building Information Extraction and Evaluation from High-resolution Remotely Sensed Data

The two-dimensional (2D) footprints and three-dimensional (3D) structures of buildings are of great importance to city planning, natural disaster management, and virtual environmental simulation. As traditional manual methodologies for collecting 2D and 3D building information are often both time consuming and costly, automated methods are required for efficient large area mapping. It is challenging to extract building information from remotely sensed data, considering the complex nature of urban environments and their associated intricate building structures. Most 2D evaluation methods are focused on classification accuracy, while other dimensions of extraction accuracy are ignored. To assess 2D building extraction methods, a multi-criteria evaluation system has been designed. The proposed system consists of matched rate, shape similarity, and positional accuracy. Experimentation with four methods demonstrates that the proposed multi-criteria system is more comprehensive and effective, in comparison with traditional accuracy assessment metrics. Building height is critical for building 3D structure extraction. As data sources for height estimation, digital surface models (DSMs) that are derived from stereo images using existing software typically provide low accuracy results in terms of rooftop elevations. Therefore, a new image matching method is proposed by adding building footprint maps as constraints. Validation demonstrates that the proposed matching method can estimate building rooftop elevation with one third of the error encountered when using current commercial software. With an ideal input DSM, building height can be estimated by the elevation contrast inside and outside a building footprint. However, occlusions and shadows cause indistinct building edges in the DSMs generated from stereo images. Therefore, a “building-ground elevation difference model” (EDM) has been designed, which describes the trend of the elevation difference between a building and its neighbours, in order to find elevation values at bare ground. Experiments using this novel approach report that estimated building height with 1.5m residual, which out-performs conventional filtering methods. Finally, 3D buildings are digitally reconstructed and evaluated. Current 3D evaluation methods did not present the difference between 2D and 3D evaluation methods well; traditionally, wall accuracy is ignored. To address these problems, this thesis designs an evaluation system with three components: volume, surface, and point. As such, the resultant multi-criteria system provides an improved evaluation method for building reconstruction

LOD Generation for Urban Scenes

International audienceWe introduce a novel approach that reconstructs 3D urban scenes in the form of levels of detail (LODs). Starting from raw data sets such as surface meshes generated by multi-view stereo systems, our algorithm proceeds in three main steps: classification, abstraction and reconstruction. From geometric attributes and a set of semantic rules combined with a Markov random field, we classify the scene into four meaningful classes. The abstraction step detects and regularizes planar structures on buildings, fits icons on trees, roofs and facades, and performs filtering and simplification for LOD generation. The abstracted data are then provided as input to the reconstruction step which generates watertight buildings through a min-cut formula-tion on a set of 3D arrangements. Our experiments on complex buildings and large scale urban scenes show that our approach generates meaningful LODs while being robust and scalable. By combining semantic segmentation and abstraction it also outperforms general mesh approximation ap-proaches at preserving urban structures

Bridge Inspection: Human Performance, Unmanned Aerial Systems and Automation

Unmanned aerial systems (UASs) have become of considerable private and commercial interest for a variety of jobs and entertainment in the past 10 years. This paper is a literature review of the state of practice for the United States bridge inspection programs and outlines how automated and unmanned bridge inspections can be made suitable for present and future needs. At its best, current technology limits UAS use to an assistive tool for the inspector to perform a bridge inspection faster, safer, and without traffic closure. The major challenges for UASs are satisfying restrictive Federal Aviation Administration regulations, control issues in a GPS-denied environment, pilot expenses and availability, time and cost allocated to tuning, maintenance, post-processing time, and acceptance of the collected data by bridge owners. Using UASs with self-navigation abilities and improving image-processing algorithms to provide results near real-time could revolutionize the bridge inspection industry by providing accurate, multi-use, autonomous three-dimensional models and damage identification