Distinguishing Buildings from Vegetation in an Urban-Chaparral Mosaic Landscape with LiDAR-Informed Discriminant Analysis
Identification of buildings from remotely sensed imagery in urban and suburban areas is a challenging task. Light detection and Ranging (LiDAR) provides an opportunity to accurately identify buildings by identification of planar surfaces. Dense vegetation can limit the number of light particles that...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/6/1703 |
| _version_ | 1827747805435789312 |
|---|---|
| author | Thomas J. Yamashita David B. Wester Michael E. Tewes John H. Young Jason V. Lombardi |
| author_facet | Thomas J. Yamashita David B. Wester Michael E. Tewes John H. Young Jason V. Lombardi |
| author_sort | Thomas J. Yamashita |
| collection | DOAJ |
| description | Identification of buildings from remotely sensed imagery in urban and suburban areas is a challenging task. Light detection and Ranging (LiDAR) provides an opportunity to accurately identify buildings by identification of planar surfaces. Dense vegetation can limit the number of light particles that reach the ground, potentially creating false planar surfaces within a vegetation stand. We present an application of discriminant analysis (a commonly used statistical tool in decision theory) to classify polygons (derived from LiDAR) as either buildings or a non-building planar surfaces. We conducted our analysis in southern Texas where thornscrub vegetation often prevents a LiDAR beam from fully penetrating the vegetation canopy in and around residential areas. Using discriminant analysis, we grouped potential building polygons into building and non-building classes using the point densities of ground, unclassified, and building points. Our technique was 95% accurate at distinguishing buildings from non-buildings. Therefore, we recommend its use in any locale where distinguishing buildings from surrounding vegetation may be affected by the proximity of dense vegetation to buildings. |
| first_indexed | 2024-03-11T05:57:26Z |
| format | Article |
| id | doaj.art-9781f9391308434aad0851b5c63783db |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T05:57:26Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9781f9391308434aad0851b5c63783db2023-11-17T13:40:48ZengMDPI AGRemote Sensing2072-42922023-03-01156170310.3390/rs15061703Distinguishing Buildings from Vegetation in an Urban-Chaparral Mosaic Landscape with LiDAR-Informed Discriminant AnalysisThomas J. Yamashita0David B. Wester1Michael E. Tewes2John H. Young3Jason V. Lombardi4Caesar Kleberg Wildlife Research Institute, Texas A&M University—Kingsville, Kingsville, TX 78363, USACaesar Kleberg Wildlife Research Institute, Texas A&M University—Kingsville, Kingsville, TX 78363, USACaesar Kleberg Wildlife Research Institute, Texas A&M University—Kingsville, Kingsville, TX 78363, USAEnvironmental Affairs Division, Texas Department of Transportation, Austin, TX 78701, USACaesar Kleberg Wildlife Research Institute, Texas A&M University—Kingsville, Kingsville, TX 78363, USAIdentification of buildings from remotely sensed imagery in urban and suburban areas is a challenging task. Light detection and Ranging (LiDAR) provides an opportunity to accurately identify buildings by identification of planar surfaces. Dense vegetation can limit the number of light particles that reach the ground, potentially creating false planar surfaces within a vegetation stand. We present an application of discriminant analysis (a commonly used statistical tool in decision theory) to classify polygons (derived from LiDAR) as either buildings or a non-building planar surfaces. We conducted our analysis in southern Texas where thornscrub vegetation often prevents a LiDAR beam from fully penetrating the vegetation canopy in and around residential areas. Using discriminant analysis, we grouped potential building polygons into building and non-building classes using the point densities of ground, unclassified, and building points. Our technique was 95% accurate at distinguishing buildings from non-buildings. Therefore, we recommend its use in any locale where distinguishing buildings from surrounding vegetation may be affected by the proximity of dense vegetation to buildings.https://www.mdpi.com/2072-4292/15/6/1703building identificationLiDARdiscriminant analysisLP360remote sensing |
| spellingShingle | Thomas J. Yamashita David B. Wester Michael E. Tewes John H. Young Jason V. Lombardi Distinguishing Buildings from Vegetation in an Urban-Chaparral Mosaic Landscape with LiDAR-Informed Discriminant Analysis Remote Sensing building identification LiDAR discriminant analysis LP360 remote sensing |
| title | Distinguishing Buildings from Vegetation in an Urban-Chaparral Mosaic Landscape with LiDAR-Informed Discriminant Analysis |
| title_full | Distinguishing Buildings from Vegetation in an Urban-Chaparral Mosaic Landscape with LiDAR-Informed Discriminant Analysis |
| title_fullStr | Distinguishing Buildings from Vegetation in an Urban-Chaparral Mosaic Landscape with LiDAR-Informed Discriminant Analysis |
| title_full_unstemmed | Distinguishing Buildings from Vegetation in an Urban-Chaparral Mosaic Landscape with LiDAR-Informed Discriminant Analysis |
| title_short | Distinguishing Buildings from Vegetation in an Urban-Chaparral Mosaic Landscape with LiDAR-Informed Discriminant Analysis |
| title_sort | distinguishing buildings from vegetation in an urban chaparral mosaic landscape with lidar informed discriminant analysis |
| topic | building identification LiDAR discriminant analysis LP360 remote sensing |
| url | https://www.mdpi.com/2072-4292/15/6/1703 |
| work_keys_str_mv | AT thomasjyamashita distinguishingbuildingsfromvegetationinanurbanchaparralmosaiclandscapewithlidarinformeddiscriminantanalysis AT davidbwester distinguishingbuildingsfromvegetationinanurbanchaparralmosaiclandscapewithlidarinformeddiscriminantanalysis AT michaeletewes distinguishingbuildingsfromvegetationinanurbanchaparralmosaiclandscapewithlidarinformeddiscriminantanalysis AT johnhyoung distinguishingbuildingsfromvegetationinanurbanchaparralmosaiclandscapewithlidarinformeddiscriminantanalysis AT jasonvlombardi distinguishingbuildingsfromvegetationinanurbanchaparralmosaiclandscapewithlidarinformeddiscriminantanalysis |