ENTITIES AND FEATURES FOR CLASSIFCATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREA
We aim at efficiently classifying ALS data in urban areas by choosing an optimal combination of features and entities. Three kinds of entities are defined, namely, single points, planar segments and segments obtained by mean-shift segmentation. Various features are computed for these three entities....
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2012-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-4/257/2012/isprsannals-I-4-257-2012.pdf |
| _version_ | 1811236988104212480 |
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| author | S. Xu S. Oude Elberink G. Vosselman |
| author_facet | S. Xu S. Oude Elberink G. Vosselman |
| author_sort | S. Xu |
| collection | DOAJ |
| description | We aim at efficiently classifying ALS data in urban areas by choosing an optimal combination of features and entities. Three kinds
of entities are defined, namely, single points, planar segments and segments obtained by mean-shift segmentation. Various features
are computed for these three entities. All derived features are assigned to different steps of our method. Our method is composed of
a sequence of rule based classifications. After a rule based classification for planar segments and a context rule based classification
for walls and roof elements 85% of the data are well classified. Errors mainly appear in the area where rules are difficult to define,
such as vegetation close to walls and above roofs. To eliminate these errors, we first group all the points in these areas into segments
using mean shift, and then search for segments with potentially misclassified points using a distance ratio. These mean shift
segments are then re-classified using another rule based classification. The overall quality of our classification method reaches to
98.1%. |
| first_indexed | 2024-04-12T12:18:12Z |
| format | Article |
| id | doaj.art-d3feedd5dae74b46abacd9fe5efe9652 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-04-12T12:18:12Z |
| publishDate | 2012-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-d3feedd5dae74b46abacd9fe5efe96522022-12-22T03:33:22ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502012-07-01I-425726210.5194/isprsannals-I-4-257-2012ENTITIES AND FEATURES FOR CLASSIFCATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREAS. Xu0S. Oude Elberink1G. Vosselman2Faculty of Geo-Information Science and Earth Observation, University of Twente, NetherlandsFaculty of Geo-Information Science and Earth Observation, University of Twente, NetherlandsFaculty of Geo-Information Science and Earth Observation, University of Twente, NetherlandsWe aim at efficiently classifying ALS data in urban areas by choosing an optimal combination of features and entities. Three kinds of entities are defined, namely, single points, planar segments and segments obtained by mean-shift segmentation. Various features are computed for these three entities. All derived features are assigned to different steps of our method. Our method is composed of a sequence of rule based classifications. After a rule based classification for planar segments and a context rule based classification for walls and roof elements 85% of the data are well classified. Errors mainly appear in the area where rules are difficult to define, such as vegetation close to walls and above roofs. To eliminate these errors, we first group all the points in these areas into segments using mean shift, and then search for segments with potentially misclassified points using a distance ratio. These mean shift segments are then re-classified using another rule based classification. The overall quality of our classification method reaches to 98.1%.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-4/257/2012/isprsannals-I-4-257-2012.pdf |
| spellingShingle | S. Xu S. Oude Elberink G. Vosselman ENTITIES AND FEATURES FOR CLASSIFCATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREA ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | ENTITIES AND FEATURES FOR CLASSIFCATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREA |
| title_full | ENTITIES AND FEATURES FOR CLASSIFCATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREA |
| title_fullStr | ENTITIES AND FEATURES FOR CLASSIFCATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREA |
| title_full_unstemmed | ENTITIES AND FEATURES FOR CLASSIFCATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREA |
| title_short | ENTITIES AND FEATURES FOR CLASSIFCATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREA |
| title_sort | entities and features for classifcation of airborne laser scanning data in urban area |
| url | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-4/257/2012/isprsannals-I-4-257-2012.pdf |
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