DETECTION AND COUNTING OF ORCHARD TREES FROM VHR IMAGES USING A GEOMETRICAL-OPTICAL MODEL AND MARKED TEMPLATE MATCHING
This article presents an original algorithm created to detect and count trees in orchards using very high resolution images. The algorithm is based on an adaptation of the “template matching” image processing approach, in which the template is based on a “geometricaloptical” model created from a ser...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-06-01
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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/III-7/75/2016/isprs-annals-III-7-75-2016.pdf |
| _version_ | 1818450448253714432 |
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| author | P. Maillard M. F. Gomes |
| author_facet | P. Maillard M. F. Gomes |
| author_sort | P. Maillard |
| collection | DOAJ |
| description | This article presents an original algorithm created to detect and count trees in orchards using very high resolution images. The algorithm
is based on an adaptation of the “template matching” image processing approach, in which the template is based on a “geometricaloptical”
model created from a series of parameters, such as illumination angles, maximum and ambient radiance, and tree size specifications.
The algorithm is tested on four images from different regions of the world and different crop types. These images all have < 1
meter spatial resolution and were downloaded from the GoogleEarth application. Results show that the algorithm is very efficient at
detecting and counting trees as long as their spectral and spatial characteristics are relatively constant. For walnut, mango and orange
trees, the overall accuracy was clearly above 90%. However, the overall success rate for apple trees fell under 75%. It appears that the
openness of the apple tree crown is most probably responsible for this poorer result. The algorithm is fully explained with a step-by-step
description. At this stage, the algorithm still requires quite a bit of user interaction. The automatic determination of most of the required
parameters is under development. |
| first_indexed | 2024-12-14T20:51:28Z |
| format | Article |
| id | doaj.art-01248353156b4538a0df71f0f741bb39 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-14T20:51:28Z |
| publishDate | 2016-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-01248353156b4538a0df71f0f741bb392022-12-21T22:47:47ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502016-06-01III-7758210.5194/isprs-annals-III-7-75-2016DETECTION AND COUNTING OF ORCHARD TREES FROM VHR IMAGES USING A GEOMETRICAL-OPTICAL MODEL AND MARKED TEMPLATE MATCHINGP. Maillard0M. F. Gomes1UFMG, Departamento de Geografia, Av. Antˆonio Carlos, 6627, Belo Horizonte - MG, BrazilUFMG, Departamento de Geografia, Av. Antˆonio Carlos, 6627, Belo Horizonte - MG, BrazilThis article presents an original algorithm created to detect and count trees in orchards using very high resolution images. The algorithm is based on an adaptation of the “template matching” image processing approach, in which the template is based on a “geometricaloptical” model created from a series of parameters, such as illumination angles, maximum and ambient radiance, and tree size specifications. The algorithm is tested on four images from different regions of the world and different crop types. These images all have < 1 meter spatial resolution and were downloaded from the GoogleEarth application. Results show that the algorithm is very efficient at detecting and counting trees as long as their spectral and spatial characteristics are relatively constant. For walnut, mango and orange trees, the overall accuracy was clearly above 90%. However, the overall success rate for apple trees fell under 75%. It appears that the openness of the apple tree crown is most probably responsible for this poorer result. The algorithm is fully explained with a step-by-step description. At this stage, the algorithm still requires quite a bit of user interaction. The automatic determination of most of the required parameters is under development.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-7/75/2016/isprs-annals-III-7-75-2016.pdf |
| spellingShingle | P. Maillard M. F. Gomes DETECTION AND COUNTING OF ORCHARD TREES FROM VHR IMAGES USING A GEOMETRICAL-OPTICAL MODEL AND MARKED TEMPLATE MATCHING ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | DETECTION AND COUNTING OF ORCHARD TREES FROM VHR IMAGES USING A GEOMETRICAL-OPTICAL MODEL AND MARKED TEMPLATE MATCHING |
| title_full | DETECTION AND COUNTING OF ORCHARD TREES FROM VHR IMAGES USING A GEOMETRICAL-OPTICAL MODEL AND MARKED TEMPLATE MATCHING |
| title_fullStr | DETECTION AND COUNTING OF ORCHARD TREES FROM VHR IMAGES USING A GEOMETRICAL-OPTICAL MODEL AND MARKED TEMPLATE MATCHING |
| title_full_unstemmed | DETECTION AND COUNTING OF ORCHARD TREES FROM VHR IMAGES USING A GEOMETRICAL-OPTICAL MODEL AND MARKED TEMPLATE MATCHING |
| title_short | DETECTION AND COUNTING OF ORCHARD TREES FROM VHR IMAGES USING A GEOMETRICAL-OPTICAL MODEL AND MARKED TEMPLATE MATCHING |
| title_sort | detection and counting of orchard trees from vhr images using a geometrical optical model and marked template matching |
| url | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-7/75/2016/isprs-annals-III-7-75-2016.pdf |
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