Contextual classification of point cloud data by exploiting individual 3d neigbourhoods

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification

The Cratering History of Asteroid (2867) Steins

The cratering history of main belt asteroid (2867) Steins has been investigated using OSIRIS imagery acquired during the Rosetta flyby that took place on the 5th of September 2008. For this purpose, we applied current models describing the formation and evolution of main belt asteroids, that provide the rate and velocity distributions of impactors. These models coupled with appropriate crater scaling laws, allow the cratering history to be estimated. Hence, we derive Steins' cratering retention age, namely the time lapsed since its formation or global surface reset. We also investigate the influence of various factors -like bulk structure and crater erasing- on the estimated age, which spans from a few hundred Myrs to more than 1Gyr, depending on the adopted scaling law and asteroid physical parameters. Moreover, a marked lack of craters smaller than about 0.6km has been found and interpreted as a result of a peculiar evolution of Steins cratering record, possibly related either to the formation of the 2.1km wide impact crater near the south pole or to YORP reshaping.Comment: Accepted by Planetary and Space Scienc

Vanadium (β-(Dimethylamino)ethyl)cyclopentadienyl Complexes with Diphenylacetylene Ligands

Reduction of the V(III) (β-(dimethylamino)ethyl)cyclopentadienyl dichloride complex [η5:η1-C5H4(CH2)2NMe2]VCl2(PMe3) with 1 equiv of Na/Hg yielded the V(II) dimer {[η5:η1-C5H4(CH2)2NMe2]V(µ-Cl)}2 (2). This compound reacted with diphenylacetylene in THF to give the V(II) alkyne adduct [η5:η1-C5H4(CH2)2NMe2]VCl(η2-PhC≡CPh). Further reduction of 2 with Mg in the presence of diphenylacetylene resulted in oxidative coupling of two diphenylacetylene groups to yield the diamagnetic, formally V(V), bent metallacyclopentatriene complex [η5:η1-C5H4(CH2)2NMe2]V(C4Ph4).

Classification of airborne laser scanning data using geometric multi-scale features and different neighbourhood types

In this paper, we address the classification of airborne laser scanning data. We present a novel methodology relying on the use of complementary types of geometric features extracted from multiple local neighbourhoods of different scale and type. To demonstrate the performance of our methodology, we present results of a detailed evaluation on a standard benchmark dataset and we show that the consideration of multi-scale, multi-type neighbourhoods as the basis for feature extraction leads to improved classification results in comparison to single-scale neighbourhoods as well as in comparison to multi-scale neighbourhoods of the same type

Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features

3D scene analysis by automatically assigning 3D points a semantic label has become an issue of major interest in recent years. Whereas the tasks of feature extraction and classification have been in the focus of research, the idea of using only relevant and more distinctive features extracted from optimal 3D neighborhoods has only rarely been addressed in 3D lidar data processing. In this paper, we focus on the interleaved issue of extracting relevant, but not redundant features and increasing their distinctiveness by considering the respective optimal 3D neighborhood of each individual 3D point. We present a new, fully automatic and versatile framework consisting of four successive steps: (i) optimal neighborhood size selection, (ii) feature extraction, (iii) feature selection, and (iv) classification. In a detailed evaluation which involves 5 different neighborhood definitions, 21 features, 6 approaches for feature subset selection and 2 different classifiers, we demonstrate that optimal neighborhoods for individual 3D points significantly improve the results of scene interpretation and that the selection of adequate feature subsets may even further increase the quality of the derived results