Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
DTM Generation from LIDAR Data using Skewness Balancing
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Unsupervised Segmentation Using Gabor Wavelets and Statistical Features in LIDAR Data Analysis
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
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Light Detection And Ranging (LIDAR) is an important modality in terrain and land surveying for many environmental, engineering and civil applications. This paper presents the framework for a recently developed unsupervised classification algorithm called Skewness Balancing for object and ground point separation in airborne LIDAR data. The main advantages of the algorithm are threshold-freedom and independence from LIDAR data format and resolution, while preserving object and terrain details. The framework for Skewness Balancing has been built in this contribution with a prediction model in which unknown LIDAR tiles can be categorised as ''hilly'' or ''moderate'' terrains. Accuracy assessment of the model is carried out using cross-validation with an overall accuracy of 95%. An extension to the algorithm is developed to address the overclassification issue for hilly terrain. For moderate terrain, the results show that from the classified tiles detached objects (buildings and vegetation) and attached objects (bridges and motorway junctions) are separated from bare earth (ground, roads and yards) which makes Skewness Balancing ideal to be integrated into geographic information system (GIS) software packages.