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Object-oriented Classification of Urban Areas Using Lidar and Aerial Images
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 Title & Authors
Object-oriented Classification of Urban Areas Using Lidar and Aerial Images
Lee, Won Hee;
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In this paper, object-based classification of urban areas based on a combination of information from lidar and aerial images is introduced. High resolution images are frequently used in automatic classification, making use of the spectral characteristics of the features under study. However, in urban areas, pixel-based classification can be difficult since building colors differ and the shadows of buildings can obscure building segmentation. Therefore, if the boundaries of buildings can be extracted from lidar, this information could improve the accuracy of urban area classifications. In the data processing stage, lidar data and the aerial image are co-registered into the same coordinate system, and a local maxima filter is used for the building segmentation of lidar data, which are then converted into an image containing only building information. Then, multiresolution segmentation is achieved using a scale parameter, and a color and shape factor; a compactness factor and a layer weight are implemented for the classification using a class hierarchy. Results indicate that lidar can provide useful additional data when combined with high resolution images in the object-oriented hierarchical classification of urban areas.
Object-oriented Classification;Lidar;Aerial Image;eCognition;Image Segmentation;Hierarchical Classification;
 Cited by
LiDAR를 이용한 농업용 무인헬기 충돌방지시스템 개발,정준호;김학성;이동우;석진영;김승균;김진구;류시대;김성남;

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