Surface classification from airborne laser scanning data

“…In addition to modeling with laser scanners only, other devices, such as panoramic cameras, are used integrated with laser scanners [23]. Besides the techniques for modeling indoor environments, especially for robot navigation, terrains are modeled using airborne laser scanners for obtaining terrestrial information as in [24] and [25]. Apart from modeling the environments above the sea level, 3-D models of seafloor are also obtained using autonomous underwater vehicles equipped with a camera, a sonar, and oceanographic sensors [26].…”

Section: -D Modeling Techniques Allow Describing Environments Includmentioning

confidence: 99%

Novel Compression Algorithm Based on Sparse Sampling of 3-D Laser Range Scans

Dobrucali

Barshan

2012

The Computer Journal

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3-D models of environments can be very useful and are commonly employed in areas such as robotics, art and architecture, environmental planning and documentation. A 3-D model is typically comprised of a large number of measurements. When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to use the capacity of the communication channel or the storage medium effectively. In this thesis, we propose a novel compression technique based on compressive sampling, applied to sparse representations of 3-D laser range measurements. The main issue here is finding highly sparse representations of the range measurements, since they do not have such representations in common domains, such as the frequency domain. To solve this problem, we develop a new algorithm to generate sparse innovations between consecutive range measurements acquired while the sensor moves. We compare the sparsity of our innovations with others generated by estimation and filtering. Furthermore, we compare the compression performance of our lossy compression method with widely used lossless and lossy compression techniques.The proposed method offers small compression ratio and provides a reasonable compromise between reconstruction error and processing time.

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“…Two ways are often utilized to identify building measurements from LIDAR data. One is to separate ground, buildings, trees, and other measurements from LIDAR data simultaneously [7] [8]. The more popular way is to separate the ground from non-ground LIDAR measurements first and then identify the building points from nonground measurements [9]- [11].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Filin [8] and Morgan and Habib [13] separated building and tree measurements using the parameters for a plane which fits a LIDAR point and its neighbors within a local window. Elberink and Mass [14] segmented LIDAR data using anisotropic height texture measures.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Construction of Building Footprints From Airborne LIDAR Data

Zhang

Yan

Chen

2006

IEEE Trans. Geosci. Remote Sensing

243

111

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Abstract-This paper presents a framework that applies a series of algorithms to automatically extract building footprints from airborne LIDAR measurements. In the proposed framework, the ground and non-ground LIDAR measurements are first separated using a progressive morphological filter. Then, building measurements are identified from non-ground measurements using a region growing algorithm based on the planefitting technique. Finally, raw footprints for segmented building measurements are derived by connecting boundary points, and the raw footprints are further simplified and adjusted to remove noise caused by irregularly spaced LIDAR measurements. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. Quantitative analysis showed that the total of omission and commission errors for extracted footprints for both institutional and residential areas was about 12%. The results demonstrated that the proposed framework identified building footprints well.

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Surface classification from airborne laser scanning data

Cited by 62 publications

References 10 publications

Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

Novel Compression Algorithm Based on Sparse Sampling of 3-D Laser Range Scans

Automatic Construction of Building Footprints From Airborne LIDAR Data

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