Joachim Niemeyer

Bio: Joachim Niemeyer is an academic researcher from Leibniz University of Hanover. The author has contributed to research in topics: Conditional random field & Point cloud. The author has an hindex of 9, co-authored 15 publications receiving 741 citations.

Contextual classification of lidar data and building object detection in urban areas

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 50 m 2 ) can be detected very reliably with a correctness larger than 96% and a completeness of 100%.

CONDITIONAL RANDOM FIELDS for LIDAR POINT CLOUD CLASSIFICATION in COMPLEX URBAN AREAS

Abstract: In this paper, we investigate the potential of a Conditional Random Field (CRF) approach for the classification of an airborne LiDAR (Light Detection And Ranging) point cloud. This method enables the incorporation of contextual information and learning of specific relations of object classes within a training step. Thus, it is a powerful approach for obtaining reliable results even in complex urban scenes. Geometrical features as well as an intensity value are used to distinguish the five object classes building , low vegetation , tree , natural ground , and asphalt ground . The performance of our method is evaluated on the dataset of Vaihingen, Germany, in the context of the 'ISPRS Test Project on Urban Classification and 3D Building Reconstruction'. Therefore, the results of the 3D classification were submitted as a 2D binary label image for a subset of two classes, namely building and tree .

Hierarchical higher order crf for the classification of airborne lidar point clouds in urban areas

Abstract: . We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the distance and the orientation of a segment with respect to the closest road . It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.

Conditional random fields for urban scene classification with full waveform LiDAR data

Abstract: We propose a context-based classification method for point clouds acquired by full waveform airborne laser scanners. As these devices provide a higher point density and additional information like echo width or type of return, an accurate distinction of several object classes is possible. However, especially in dense urban areas correct labelling is a challenging task. Therefore, we incorporate context knowledge by using Conditional Random Fields. Typical object structures are learned in a training step and improve the results of the point-based classification process. We validate our approach with two real-world datasets and by a comparison to Support Vector Machines and Markov Random Fields.

Classification of urban LiDAR data using conditional random field and random forests

Abstract: In this work we address the task of contextual classification of an airborne LiDAR point cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random Field (CRF) framework. A CRF has been shown to deliver good results discerning multiple classes. It is a flexible approach for obtaining a reliable classification even in complex urban scenes. The incorporation of multi-scale features improves the results further. Based on the classification results, 2D building and tree objects are generated and evaluated by the benchmark of ISPRS WG III/4.

Random forest in remote sensing: A review of applications and future directions

Abstract: A random forest (RF) classifier is an ensemble classifier that produces multiple decision trees, using a randomly selected subset of training samples and variables. This classifier has become popular within the remote sensing community due to the accuracy of its classifications. The overall objective of this work was to review the utilization of RF classifier in remote sensing. This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting. It is, however, sensitive to the sampling design. The variable importance (VI) measurement provided by the RF classifier has been extensively exploited in different scenarios, for example to reduce the number of dimensions of hyperspectral data, to identify the most relevant multisource remote sensing and geographic data, and to select the most suitable season to classify particular target classes. Further investigations are required into less commonly exploited uses of this classifier, such as for sample proximity analysis to detect and remove outliers in the training samples.

Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs

Abstract: We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points. We argue that the organization of 3D point clouds can be efficiently captured by a structure called superpoint graph (SPG), derived from a partition of the scanned scene into geometrically homogeneous elements. SPGs offer a compact yet rich representation of contextual relationships between object parts, which is then exploited by a graph convolutional network. Our framework sets a new state of the art for segmenting outdoor LiDAR scans (+11.9 and +8.8 mIoU points for both Semantic3D test sets), as well as indoor scans (+12.4 mIoU points for the S3DIS dataset).

Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

Abstract: 3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.

Contextual classification of lidar data and building object detection in urban areas

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 50 m 2 ) can be detected very reliably with a correctness larger than 96% and a completeness of 100%.