OBJECT-BASED CLASSIFICATION OF URBAN AIRBORNE LIDAR POINT CLOUDS WITH MULTIPLE ECHOES USING SVM
Airborne LiDAR point clouds classification is meaningful for various applications. In this paper, an object-based analysis method is proposed to classify the point clouds in urban areas. In the process of classification, outliers in the point clouds are first removed. Second, surface growing algor...
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doaj-83655061c07146a3b26e5f643b5aa8272020-11-24T22:48:09ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502012-07-01I-313514010.5194/isprsannals-I-3-135-2012OBJECT-BASED CLASSIFICATION OF URBAN AIRBORNE LIDAR POINT CLOUDS WITH MULTIPLE ECHOES USING SVMJ. X. Zhang0X. G. Lin1Key Laboratory of Mapping from Space of State Bureau of Surveying and Mapping, Chinese Academy of Surveying and Mapping, No. 28, Lianhuachixi Road, Haidian District, Beijing 100830, ChinaKey Laboratory of Mapping from Space of State Bureau of Surveying and Mapping, Chinese Academy of Surveying and Mapping, No. 28, Lianhuachixi Road, Haidian District, Beijing 100830, ChinaAirborne LiDAR point clouds classification is meaningful for various applications. In this paper, an object-based analysis method is proposed to classify the point clouds in urban areas. In the process of classification, outliers in the point clouds are first removed. Second, surface growing algorithm is employed to segment the point clouds into different clusters. The above point cloud segmentation is helpful to derive useful features such as average height, size/area, proportion of multiple echoes, slope/orientation, elevation difference, rectangularity, ratio of length to width, and compactness. At last, SVM-based classification is performed on the segmented point clouds with radial basis function as kernel. Two datasets with high point densities are employed to test the proposed method, and three classes are predefined. The results suggest that our method will produce the overall classification accuracy larger than 97% and the Kappa coefficient larger than 0.95.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/135/2012/isprsannals-I-3-135-2012.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
J. X. Zhang X. G. Lin |
spellingShingle |
J. X. Zhang X. G. Lin OBJECT-BASED CLASSIFICATION OF URBAN AIRBORNE LIDAR POINT CLOUDS WITH MULTIPLE ECHOES USING SVM ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
J. X. Zhang X. G. Lin |
author_sort |
J. X. Zhang |
title |
OBJECT-BASED CLASSIFICATION OF URBAN AIRBORNE LIDAR POINT CLOUDS WITH MULTIPLE ECHOES USING SVM |
title_short |
OBJECT-BASED CLASSIFICATION OF URBAN AIRBORNE LIDAR POINT CLOUDS WITH MULTIPLE ECHOES USING SVM |
title_full |
OBJECT-BASED CLASSIFICATION OF URBAN AIRBORNE LIDAR POINT CLOUDS WITH MULTIPLE ECHOES USING SVM |
title_fullStr |
OBJECT-BASED CLASSIFICATION OF URBAN AIRBORNE LIDAR POINT CLOUDS WITH MULTIPLE ECHOES USING SVM |
title_full_unstemmed |
OBJECT-BASED CLASSIFICATION OF URBAN AIRBORNE LIDAR POINT CLOUDS WITH MULTIPLE ECHOES USING SVM |
title_sort |
object-based classification of urban airborne lidar point clouds with multiple echoes using svm |
publisher |
Copernicus Publications |
series |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
2194-9042 2194-9050 |
publishDate |
2012-07-01 |
description |
Airborne LiDAR point clouds classification is meaningful for various applications. In this paper, an object-based analysis method is
proposed to classify the point clouds in urban areas. In the process of classification, outliers in the point clouds are first removed.
Second, surface growing algorithm is employed to segment the point clouds into different clusters. The above point cloud
segmentation is helpful to derive useful features such as average height, size/area, proportion of multiple echoes, slope/orientation,
elevation difference, rectangularity, ratio of length to width, and compactness. At last, SVM-based classification is performed on the
segmented point clouds with radial basis function as kernel. Two datasets with high point densities are employed to test the proposed
method, and three classes are predefined. The results suggest that our method will produce the overall classification accuracy larger
than 97% and the Kappa coefficient larger than 0.95. |
url |
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/135/2012/isprsannals-I-3-135-2012.pdf |
work_keys_str_mv |
AT jxzhang objectbasedclassificationofurbanairbornelidarpointcloudswithmultipleechoesusingsvm AT xglin objectbasedclassificationofurbanairbornelidarpointcloudswithmultipleechoesusingsvm |
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1725679327767429120 |