Hanyun Wang

Learn Multiple-Kernel SVMs for Domain Adaptation in Hyperspectral Data

This letter presents a novel semisupervised method for addressing a domain adaptation problem in the classification of hyperspectral data. To overcome the influence of distribution bias between the source and target domains, we introduce the domain transfer multiple-kernel learning to simultaneously minimize the maximum mean discrepancy criterion and the structural risk functional of support vector machines. Then, the pairwise binary classifiers are merged as the multiclass classifier for solving the classification problem in hyperspectral data. Both bias and nonbias sampling strategies are introduced to evaluate the robustness of the proposed method against the spectral distribution bias. The results obtained from real data sets show that the proposed method can achieve higher classification accuracy even with cross-domain distribution bias and provide robust solutions with different labeled and unlabeled data sizes.

Object Detection in Terrestrial Laser Scanning Point Clouds Based on Hough Forest

This letter presents a novel rotation-invariant method for object detection from terrestrial 3-D laser scanning point clouds acquired in complex urban environments. We utilize the Implicit Shape Model to describe object categories, and extend the Hough Forest framework for object detection in 3-D point clouds. A 3-D local patch is described by structure and reflectance features and then mapped to the probabilistic vote about the possible location of the object center. Objects are detected at the peak points in the 3-D Hough voting space. To deal with the arbitrary azimuths of objects in real world, circular voting strategy is introduced by rotating the offset vector. To deal with the interference of adjacent objects, distance weighted voting is proposed. Large-scale real-world point cloud data collected by terrestrial mobile laser scanning systems are used to evaluate the performance. Experimental results demonstrate that the proposed method outperforms the state-of-the-art 3-D object detection methods.

Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning Data

The assessment of pavement cracks is one of the essential tasks for road maintenance. This paper presents a novel framework, called ITVCrack, for automated crack extraction based on iterative tensor voting (ITV), from high-density point clouds collected by a mobile laser scanning system. The proposed ITVCrack comprises the following: 1) the preprocessing involving the separation of road points from nonroad points using vehicle trajectory data; 2) the generation of the georeferenced feature (GRF) image from the road points; and 3) the ITV-based crack extraction from the noisy GRF image, followed by an accurate delineation of the curvilinear cracks. Qualitatively, the method is applicable for pavement cracks with low contrast, low signal-to-noise ratio, and bad continuity. Besides the application to GRF images, the proposed framework demonstrates much better crack extraction performance when quantitatively compared to existing methods on synthetic data and pavement images.

Spatial-Related Traffic Sign Inspection for Inventory Purposes Using Mobile Laser Scanning Data

This paper presents a spatial-related traffic sign inspection process for sign type, position, and placement using mobile laser scanning (MLS) data acquired by a RIEGL VMX-450 system and presents its potential for traffic sign inventory applications. First, the paper describes an algorithm for traffic sign detection in complicated road scenes based on the retroreflectivity properties of traffic signs in MLS point clouds. Then, a point cloud-to-image registration process is proposed to project the traffic sign point clouds onto a 2-D image plane. Third, based on the extracted traffic sign points, we propose a traffic sign position and placement inspection process by creating geospatial relations between the traffic signs and road environment. For further inventory applications, we acquire several spatial-related inventory measurements. Finally, a traffic sign recognition process is conducted to assign sign type. With the acquired sign type, position, and placement data, a spatial-associated sign network is built. Experimental results indicate satisfactory performance of the proposed detection, recognition, position, and placement inspection algorithms. The experimental results also prove the potential of MLS data for automatic traffic sign inventory applications.

Patch-Based Semantic Labeling of Road Scene Using Colorized Mobile LiDAR Point Clouds

Semantic labeling of road scenes using colorized mobile LiDAR point clouds is of great significance in a variety of applications, particularly intelligent transportation systems. However, many challenges, such as incompleteness of objects caused by occlusion, overlapping between neighboring objects, interclass local similarities, and computational burden brought by a huge number of points, make it an ongoing open research area. In this paper, we propose a novel patch-based framework for labeling road scenes of colorized mobile LiDAR point clouds. In the proposed framework, first, three-dimensional (3-D) patches extracted from point clouds are used to construct a 3-D patch-based match graph structure (3D-PMG), which transfers category labels from labeled to unlabeled point cloud road scenes efficiently. Then, to rectify the transferring errors caused by local patch similarities in different categories, contextual information among 3-D patches is exploited by combining 3D-PMG with Markov random fields. In the experiments, the proposed framework is validated on colorized mobile LiDAR point clouds acquired by the RIEGL VMX-450 mobile LiDAR system. Comparative experiments show the superior performance of the proposed framework for accurate semantic labeling of road scenes.

Road Boundaries Detection Based on Local Normal Saliency From Mobile Laser Scanning Data

The accurate extraction of roads is a prerequisite for the automatic extraction of other road features. This letter describes a method for detecting road boundaries from mobile laser scanning (MLS) point clouds in an urban environment. The key idea of our method is directly constructing a saliency map on 3-D unorganized point clouds to extract road boundaries. The method consists of four major steps, i.e., road partition with the assistance of the vehicle trajectory, salient map construction and salient points extraction, curb detection and curb lowest points extraction, and road boundaries fitting. The performance of the proposed method is evaluated on the point clouds of an urban scene collected by a RIEGL VMX-450 MLS system. The completeness, correctness, and quality of the extracted road boundaries are 95.41%, 99.35%, and 94.81%, respectively. Experimental results demonstrate that our method is feasible for detecting road boundaries in MLS point clouds.

3-D Point Cloud Object Detection Based on Supervoxel Neighborhood With Hough Forest Framework

Object detection in three-dimensional (3-D) laser scanning point clouds of complex urban environment is a challenging problem. Existing methods are limited by their robustness to complex situations such as occlusion, overlap, and rotation or by their computational efficiency. This paper proposes a high computationally efficient method integrating supervoxel with Hough forest framework for detecting objects from 3-D laser scanning point clouds. First, a point cloud is over-segmented into spatially consistent supervoxels. Each supervoxel together with its first-order neighborhood is grouped into one local patch. All the local patches are described by both structure and reflectance features, and then used in the training stage for learning a random forest classifier as well as the detection stage to vote for the possible location of the object center. Second, local reference frame and circular voting strategies are introduced to achieve the invariance to the azimuth rotation of objects. Finally, objects are detected at the peak points in 3-D Hough voting space. The performance of our proposed method is evaluated on real-world point cloud data collected by the up-to-date mobile laser scanning system. Experimental results demonstrate that our proposed method outperforms state-of-the-art 3-D object detection methods with high computational efficiency.

Vehicle Detection in High-Resolution Aerial Images Based on Fast Sparse Representation Classification and Multiorder Feature

This paper presents an algorithm for vehicle detection in high-resolution aerial images through a fast sparse representation classification method and a multiorder feature descriptor that contains information of texture, color, and high-order context. To speed up computation of sparse representation, a set of small dictionaries, instead of a large dictionary containing all training items, is used for classification. To extract the context information of a patch, we proposed a high-order context information extraction method based on the proposed fast sparse representation classification method. To effectively extract the color information, the RGB color space is transformed into color name space. Then, the color name information is embedded into the grids of histogram of oriented gradient feature to represent the low-order feature of vehicles. By combining low- and high-order features together, a multiorder feature is used to describe vehicles. We also proposed a sample selection strategy based on our fast sparse representation classification method to construct a complete training subset. Finally, a set of dictionaries, which are trained by the multiorder features of the selected training subset, is used to detect vehicles based on superpixel segmentation results of aerial images. Experimental results illustrate the satisfactory performance of our algorithm.

Automatic road extraction from mobile laser scanning data

Extraction of road surface and boundary is essential for autonomous vehicle navigation, road monitoring and important scene structures extraction. Mobile laser scanning (MLS) technology as a new information acquiring manner can quickly scan the whole scene and provide density and accurate 3D coordinate data and other information such as trajectory, color and reflectance. In this paper an automatic road extraction method is proposed based on trajectory information from mobile laser scanning data. Through the trajectory, location and approximated direction of local road patch could be determined. Searching algorithm is applied along the approximated road direction and the orthogonal direction. To determine the road boundary, a hypothesis testing method based on local altitude variance is used. To filter false boundary points, local altitude mean value is applied. Experiment results demonstrate the reliability of the proposed algorithm for automatic road surface and boundary extraction.

Scale invariant kernel-based object tracking

Traditional kernel-based object tracking methods are useful for estimating the position of objects, but inadequate for estimating the scale of objects. In this paper, we propose a novel scale invariant kernel-based object tracking (SIKBOT) algorithm for tracking fast scaling objects through image sequences. We exploit the set property of regions and propose a new method to estimate the potential of the intersection of the object and the kernel. Regarding robustness, we iteratively estimate the scale of the object by means of basic set analysis. The scale and position of objects are simultaneously estimated by mean shift procedures in parallel. The proposed SIKBOT algorithm is demonstrated by extensive experiments on challenging real-world image sequences.