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Lidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported. [ABSTRACT FROM AUTHOR]

Abstract: State-of-the-art airborne SAR sensors provide high-resolution ground mapping data. This offers the opportunity of using this technology for the analysis of built-up areas. However, especially at building locations different SAR specific phenomena like layover, shadow, and multipath-propagation burden the interpretation of the SAR imagery even for experts. In order to consider such effects in the analysis and the geocoding of SAR data, high-resolution information about the 3D structure of the urban scene is required. Three dimensional elevation data from a GIS can be provided in different representations, like DEM (raster data) or city models (vector data). In this paper, the benefit of GIS data for the SAR mission planning and the analysis of acquired SAR data are discussed. For the first task, the SAR acquisition parameters are optimized a priori with respect to the best mapping of a local area or a certain object class. For this optimization, a large number of simulations with systematically varying aspect and viewing angles are carried out. For the second task, the 2D and 3D context information are fused with the acquired SAR imagery to support the interpretation for a change detection task. Additionally, simulated features of man-made objects are offered to the image interpreter for comparison with the data. The feasibility of different kinds of GIS data for these purposes is discussed. [Copyright &y& Elsevier]

Abstract: In this work we address the task of the contextual classification of an airborne LiDAR point cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random Field (CRF) framework. It is a flexible approach for obtaining a reliable classification result even in complex urban scenes. In this way, we benefit from the consideration of context on the one hand and from the opportunity to use a large amount of features on the other hand. Considering the interactions in our experiments increases the overall accuracy by 2%, though a larger improvement becomes apparent in the completeness and correctness of some of the seven classes discerned in our experiments. We compare the Random Forest approach to linear models for the computation of unary and pairwise potentials of the CRF, and investigate the relevance of different features for the LiDAR points as well as for the interaction of neighbouring points. In a second step, building objects are detected based on the classified point cloud. For that purpose, the CRF probabilities for the classes are plugged into a Markov Random Field as unary potentials, in which the pairwise potentials are based on a Potts model. The 2D binary building object masks are extracted and evaluated by the benchmark ISPRS Test Project on Urban Classification and 3D Building Reconstruction. The evaluation shows that the main buildings (larger than 50m2) can be detected very reliably with a correctness larger than 96% and a completeness of 100%. [Copyright &y& Elsevier]

Abstract: The Wadden Sea is a unique habitat formed by the strong influence of tidal currents. Twice a day the area is flooded and falls dry afterwards. Due to the force of tidal streams, strong morphologic changes occur frequently. In order to monitor these changes, high precision digital terrain models (DTMs) are required. Lidar proved to be an adequate technique to deliver highly accurate 3D mass points of the surface and dense spacing. However, water often remains within tidal channels and depressions even at low tide, and near infrared lidar is not able to penetrate the water leading to a point cloud which contains surface and water points. Thus, the standard processing workflow for DTM generation from lidar is not suited for the Wadden Sea. In this article, a new workflow is proposed for DTM generation from lidar data in the Wadden Sea. Two major building blocks of this workflow, namely classification of the water points and structure line detection, are presented in detail. For both tasks suitable algorithms were developed tailored to meet special requirements of mudflat. Lidar measurements from water surfaces are detected by a supervised fuzzy classification using the features height, intensity, and 2D point density. Structure lines are derived through a piecewise reconstruction of the surface from the lidar data with a hyperbolic tangent function. The obtained results show that both methods considerably improve the accuracy of DTMs from lidar data. [Copyright &y& Elsevier]