Classification of airborne laser scanning point clouds based on binomial logistic regression analysis
نویسندگان
چکیده
منابع مشابه
Automated Classification of Airborne Laser Scanning Point Clouds
Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly “see” more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protectio...
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This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning...
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Airborne laser scanning (ALS) is increasingly becoming a standard method for the collection of dense elevation models, especially in 3D urban mapping. However, automation in processing of ALS point-clouds involves handling huge datasets, irregular point distribution, multiple views, and relatively low textured surfaces. Since raster data structure is the most commonly used data representation m...
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Airborne laser scanning (ALS) is a remote sensing technique well-suited for 3D vegetation mapping and structure characterization because the emitted laser pulses are able to penetrate small gaps in the vegetation canopy. The backscattered echoes from the foliage, woody vegetation, the terrain, and other objects are detected, leading to a cloud of points. Higher echo densities (> 20 echoes/m2) a...
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Airborne Laser Scanning (ALS), also known as Light Detection and Ranging (LiDAR) enables an accurate three-dimensional characterization of vertical forest structure. ALS has proven to be an information-rich asset for forest managers, enabling the generation of highly detailed bare earth digital elevation models (DEMs) as well as estimation of a range of forest inventory attributes (including he...
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ژورنال
عنوان ژورنال: International Journal of Remote Sensing
سال: 2014
ISSN: 0143-1161,1366-5901
DOI: 10.1080/01431161.2014.904973