Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data
In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold v...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2011-06-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/3/6/1188/ |
| _version_ | 1818971238476808192 |
|---|---|
| author | Javier Estornell Jorge A. Recio Txomin Hermosilla Luis A. Ruiz |
| author_facet | Javier Estornell Jorge A. Recio Txomin Hermosilla Luis A. Ruiz |
| author_sort | Javier Estornell |
| collection | DOAJ |
| description | In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered. |
| first_indexed | 2024-12-20T14:49:12Z |
| format | Article |
| id | doaj.art-b706420c034d488e87ff536ca4b9cf22 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T14:49:12Z |
| publishDate | 2011-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b706420c034d488e87ff536ca4b9cf222022-12-21T19:37:01ZengMDPI AGRemote Sensing2072-42922011-06-01361188121010.3390/rs3061188Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR DataJavier EstornellJorge A. RecioTxomin HermosillaLuis A. RuizIn this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.http://www.mdpi.com/2072-4292/3/6/1188/building detectionLiDARhigh spatial resolution imageryobject-based image classification |
| spellingShingle | Javier Estornell Jorge A. Recio Txomin Hermosilla Luis A. Ruiz Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data Remote Sensing building detection LiDAR high spatial resolution imagery object-based image classification |
| title | Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data |
| title_full | Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data |
| title_fullStr | Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data |
| title_full_unstemmed | Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data |
| title_short | Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data |
| title_sort | evaluation of automatic building detection approaches combining high resolution images and lidar data |
| topic | building detection LiDAR high spatial resolution imagery object-based image classification |
| url | http://www.mdpi.com/2072-4292/3/6/1188/ |
| work_keys_str_mv | AT javierestornell evaluationofautomaticbuildingdetectionapproachescombininghighresolutionimagesandlidardata AT jorgearecio evaluationofautomaticbuildingdetectionapproachescombininghighresolutionimagesandlidardata AT txominhermosilla evaluationofautomaticbuildingdetectionapproachescombininghighresolutionimagesandlidardata AT luisaruiz evaluationofautomaticbuildingdetectionapproachescombininghighresolutionimagesandlidardata |