A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models

This work presents a framework for automatic feature extraction from images using stochastic geometry. Features in images are modeled as realizations of a spatial point process of geometrical shapes. This framework allows the incorporation of a prior knowledge on the spatial repartition of features. More specifically, we present a model based on the superposition of a process of segments and a process of rectangles. The former is dedicated to the detection of linear networks of discontinuities, while the latter aims at segmenting homogeneous areas. An energy is defined, favoring connections of segments, alignments of rectangles, as well as a relevant interaction between both types of objects. The estimation is performed by minimizing the energy using a simulated annealing algorithm. The proposed model is applied to the analysis of Digital Elevation Models (DEMs). These images are raster data representing the altimetry of a dense urban area. We present results on real data provided by the IGN (French National Geographic Institute) consisting in low quality DEMs of various types

A graph based model for the detection of tidal channels using marked point processes

In this paper we propose a new method for the automatic extraction of tidal channels in digital terrain models (DTM) using a sampling approach based on marked point processes. In our model, the tidal channel system is represented by an undirected, acyclic graph. The graph is iteratively generated and fitted to the data using stochastic optimization based on a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler and simulated annealing. The nodes of the graph represent junction points of the channel system and the edges straight line segments with a certain width in between. In each sampling step, the current configuration of nodes and edges is modified. The changes are accepted or rejected depending on the probability density function for the configuration which evaluates the conformity of the current status with a pre-defined model for tidal channels. In this model we favour high DTM gradient magnitudes at the edge borders and penalize a graph configuration consisting of non-connected components, overlapping segments and edges with atypical intersection angles. We present the method of our graph based model and show results for lidar data, which serve of a proof of concept of our approach.Ministry of Environment, Energy, and Climate ProtectionMinistry of Science and Culture of Lower Saxon

Urban Traffic Monitoring from LIDAR Data with a Two-Level Marked Point Process Model

In this report we present a new object based hierarchical model for joint probabilistic extraction of vehicles and coherent vehicle groups - called traffic segments - in airborne and terrestrial LIDAR point clouds collected from crowded urban areas. Firstly, the 3D point set is segmented into terrain, vehicle, roof, vegetation and clutter classes. Then the points with the corresponding class labels and intensity values are projected to the ground plane. In the obtained 2D class and intensity maps we approximate the top view projections of vehicles by rectangles. Since our tasks are simultaneously the extraction of the rectangle population which describes the position, size and orientation of the vehicles and grouping the vehicles into the traffic segments, we propose a hierarchical, Two-Level Marked Point Process (L2MPP) model for the problem. The output vehicle and traffic segment configurations are extracted by an iterative stochastic optimization algorithm. We have tested the proposed method with real aerial and terrestrial LiDAR measurements. Our aerial data set contains 471 vehicles, and we provide quantitative object and pixel level comparions results versus two state-of-the-art solutions

Extraction of Vehicle Groups in Airborne Lidar Point Clouds with Two-Level Point Processes

In this paper we present a new object based hierarchical model for joint probabilistic extraction of vehicles and groups of corresponding vehicles - called traffic segments - in airborne Lidar point clouds collected from dense urban areas. Firstly, the 3-D point set is classified into terrain, vehicle, roof, vegetation and clutter classes. Then the points with the corresponding class labels and echo strength (i.e. intensity) values are projected to the ground. In the obtained 2-D class and intensity maps we approximate the top view projections of vehicles by rectangles. Since our tasks are simultaneously the extraction of the rectangle population which describes the position, size and orientation of the vehicles and grouping the vehicles into the traffic segments, we propose a hierarchical, Two-Level Marked Point Process (L2MPP) model for the problem. The output vehicle and traffic segment configurations are extracted by an iterative stochastic optimization algorithm. We have tested the proposed method with real data of a discrete return Lidar sensor providing up to four range measurements for each laser pulse. Using manually annotated Ground Truth information on a data set containing 1009 vehicles, we provide quantitative evaluation results showing that the L2MPP model surpasses two earlier grid-based approaches, a 3-D point-cloud-based process and a single layer MPP solution. The accuracy of the proposed method measured in F-rate is 97% at object level, 83% at pixel level and 95% at group level

Building Footprint Extraction from LiDAR Data and Imagery Information

This study presents an automatic method for regularisation of building outlines. Initially, building segments are extracted using a new fusion method. Data- and model-driven approaches are then combined to generate approximate building polygons. The core part of the method includes a novel data-driven algorithm based on likelihood equation derived from the geometrical properties of a building. Finally, the Gauss-Helmert and Gauss-Markov models adjustment are implemented and modified for regularisation of building outlines considering orthogonality constraints

An Embedded Marked Point Process Framework for Three-Level Object Population Analysis

In this paper we introduce a probabilistic approach for extracting complex hierarchical object structures from digital images used by various vision applications. The proposed framework extends conventional Marked Point Process (MPP) models by (i) admitting object-subobject ensembles in parent-child relationships and (ii) allowing corresponding objects to form coherent object groups, by a Bayesian segmentation of the population. Different from earlier, highly domain specific attempts on MPP generalization, the proposed model is defined at an abstract level, providing clear interfaces for applications in various domains. We also introduce a global optimization process for the multi-layer framework for finding optimal entity configurations, considering the observed data, prior knowledge, and interactions between the neighboring and the hierarchically related objects. The proposed method is demonstrated in three different application areas: built in area analysis in remotely sensed images, traffic monitoring on airborne and mobile laser scanning (Lidar) data and optical circuit inspection. A new benchmark database is published for the three test cases, and the model's performance is quantitatively evaluated

Automatic building extraction from DEMs using an object approach and application to the 3D-city modeling

International audienceIn this paper, we present an automatic building extraction method from Digital Elevation Models based on an object approach. First, a rough approximation of the building footprints is realized by a method based on marked point processes: the building footprints are modeled by rectangle layouts. Then, these rectangular footprints are regularized by improving the connection between the neighboring rectangles and detecting the roof height discontinuities. The obtained building footprints are structured footprints: each element represents a specific part of an urban structure. Results are finally applied to a 3D-city modeling process

Automatic 3-D Building Model Reconstruction from Very High Resolution Stereo Satellite Imagery

Recent advances in the availability of very high-resolution (VHR) satellite data together with efficient data acquisition and large area coverage have led to an upward trend in their applications for automatic 3-D building model reconstruction which require large-scale and frequent updates, such as disaster monitoring and urban management. Digital Surface Models (DSMs) generated from stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting in rough building shape representations. To handle 3-D building model reconstruction using such low-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs together with orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satellite imagery. The algorithm consists of multiple steps including building boundary extraction and decomposition, image-based roof type classification, and initial roof parameter computation which are prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM (nDSM) and to select the best one, a parameter optimization method based on exhaustive search is used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building block are intersected to reconstruct the 3-D model of connecting roofs. All corresponding experiments are conducted on a dataset including four different areas of Munich city containing 208 buildings with different degrees of complexity. The results are evaluated both qualitatively and quantitatively. According to the results, the proposed approach can reliably reconstruct 3-D building models, even the complex ones with several inner yards and multiple orientations. Furthermore, the proposed approach provides a high level of automation by limiting the number of primitive roof types and by performing automatic parameter initialization