Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using Multi-Core Architectures

Kang, Xiaochen; Liu, Jiping; Lin, Xiangguo

doi:10.3390/rs6087212

Cited by 19 publications

(13 citation statements)

References 46 publications

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“…In the future, enhanced results might be achieved with the aid of the pulse intensity and data fusion strategy, and vehicles under trees may be focused on. Moreover, parallel computing [30] will be adopted to speed up the efficiency.…”

Section: Discussionmentioning

confidence: 99%

Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method

et al. 2014

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Automatic vehicle extraction from an airborne laser scanning (ALS) point cloud is very useful for many applications, such as digital elevation model generation and 3D building reconstruction. In this article, an object-based point cloud analysis (OBPCA) method is proposed for vehicle extraction from an ALS point cloud. First, a segmentation-based progressive TIN (triangular irregular network) densification is employed to detect the ground points, and the potential vehicle points are detected based on the normalized heights of the non-ground points. Second, 3D connected component analysis is performed to group the potential vehicle points into segments. At last, vehicle segments are detected based on three features, including area, rectangularity and elongatedness. Experiments suggest that the proposed method is capable of achieving higher accuracy than the exiting mean-shift-based method for vehicle extraction from an ALS point cloud. Moreover, the larger the point density is, the higher the achieved accuracy is.

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Section: Discussionmentioning

confidence: 99%

Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method

et al. 2014

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“…Besides, stream computation was also used in seamless building reconstruction from huge aerial LiDAR point sets by storing data as stream files on hard disk [18]. Kang et al decomposed the point clouds into overlapping blocks with some distance expansions, and then take advantage of multi-core computing facilities to speed up the progressive TIN densification (PTD) filter [19]. The above work focused on how to decompose the huge point dataset into an organized data stream, and then process each data element continuously.…”

Section: Related Spatial Processingmentioning

confidence: 99%

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

et al. 2017

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Surface area estimation is a widely used tool for resource evaluation in the physical world. When processing large scale spatial data, the input/output (I/O) can easily become the bottleneck in parallelizing the algorithm due to the limited physical memory resources and the very slow disk transfer rate. In this paper, we proposed a stream tilling approach to surface area estimation that first decomposed a spatial data set into tiles with topological expansions. With these tiles, the one-to-one mapping relationship between the input and the computing process was broken. Then, we realized a streaming framework towards the scheduling of the I/O processes and computing units. Herein, each computing unit encapsulated a same copy of the estimation algorithm, and multiple asynchronous computing units could work individually in parallel. Finally, the performed experiment demonstrated that our stream tilling estimation can efficiently alleviate the heavy pressures from the I/O-bound work, and the measured speedup after being optimized have greatly outperformed the directly parallel versions in shared memory systems with multi-core processors.

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“…Point cloud data filtering distinguishes ground points from non-ground points and has recently become a relatively mature technology. We used the commercial software TerraSolid to filter the data with the following basic principles [26][27][28]. First, a sparse triangulated irregular network (TIN) is first created from seed points.…”

Section: Data Preprocessingmentioning

confidence: 99%

Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

Pang

Wang³

et al. 2014

Remote Sensing

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Building change detection is useful for land management, disaster assessment, illegal building identification, urban growth monitoring, and geographic information database updating. This study proposes an automatic method that applies object-based analysis to multi-temporal point cloud data to detect building changes. The aim of this building change detection method is to identify areas that have changed and to obtain from-to information. In this method, the data are first preprocessed to generate two sets of digital surface models (DSMs), digital elevation models, and normalized DSMs from registered old and new point cloud data. Thereafter, on the basis of differential DSM, candidates for changed building objects are identified from the points in the smooth areas by using a connected component analysis technique. The random sample consensus fitting algorithm is then used to distinguish the true changed buildings from trees. The changed building objects are classified as "newly built", "taller", "demolished" or "lower" by using rule-based analysis. Finally, a test data set consisting of many buildings of different types in an 8.5 km 2 area is selected for the experiment. In the test data set, the method correctly detects 97.8% of buildings larger than 50 m 2 . The accuracy of the method is 91.2%. Furthermore, to decrease the workload of subsequent manual checking of the result, the confidence index for each changed object is computed on the basis of object features.

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Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using Multi-Core Architectures

Cited by 19 publications

References 46 publications

Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method

Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

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