Land-cover classification with an expert classification algorithm using digital aerial photographs
The purpose of this study was to evaluate the usefulness of the spectral information of digital aerial sensors in determining land-cover classification using new digital techniques. The land covers that have been evaluated are the following, (1) bare soil, (2) cereals, including maize (Zea mays L.),...
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| Format: | Article |
| Language: | English |
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Academy of Science of South Africa
2010-06-01
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| Series: | South African Journal of Science |
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| Online Access: | https://sajs.co.za/article/view/10154 |
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| author | Alberto Perea José Meroño María Aguilera José de la Cruz |
| author_facet | Alberto Perea José Meroño María Aguilera José de la Cruz |
| author_sort | Alberto Perea |
| collection | DOAJ |
| description | The purpose of this study was to evaluate the usefulness of the spectral information of digital aerial sensors in determining land-cover classification using new digital techniques. The land covers that have been evaluated are the following, (1) bare soil, (2) cereals, including maize (Zea mays L.), oats (Avena sativa L.), rye (Secale cereale L.), wheat (Triticum aestivum L.) and barley (Hordeun vulgare L.), (3) high protein crops, such as peas (Pisum sativum L.) and beans (Vicia faba L.), (4) alfalfa (Medicago sativa L.), (5) woodlands and scrublands, including holly oak (Quercus ilex L.) and common retama (Retama sphaerocarpa L.), (6) urban soil, (7) olive groves (Olea europaea L.) and (8) burnt crop stubble. The best result was obtained using an expert classification algorithm, achieving a reliability rate of 95%. This result showed that the images of digital airborne sensors hold considerable promise for the future in the field of digital classifications because these images contain valuable information that takes advantage of the geometric viewpoint. Moreover, new classification techniques reduce problems encountered using high-resolution images; while reliabilities are achieved that are better than those achieved with traditional methods. |
| format | Article |
| id | doaj-art-ef8bd58c0ad84ecd9f9653bf79abcbfc |
| institution | OA Journals |
| issn | 1996-7489 |
| language | English |
| publishDate | 2010-06-01 |
| publisher | Academy of Science of South Africa |
| record_format | Article |
| series | South African Journal of Science |
| spelling | doaj-art-ef8bd58c0ad84ecd9f9653bf79abcbfc2025-08-20T01:53:29ZengAcademy of Science of South AfricaSouth African Journal of Science1996-74892010-06-011065/66 pages6 pages8356Land-cover classification with an expert classification algorithm using digital aerial photographsAlberto Perea0José Meroño1María Aguilera2José de la Cruz3Department of Applied Physics, University of CordobaDepartment of Graphics Engineering and Geomatics, University of CordobaDepartment of Applied Physics, University of CordobaDepartment of Applied Physics, University of CordobaThe purpose of this study was to evaluate the usefulness of the spectral information of digital aerial sensors in determining land-cover classification using new digital techniques. The land covers that have been evaluated are the following, (1) bare soil, (2) cereals, including maize (Zea mays L.), oats (Avena sativa L.), rye (Secale cereale L.), wheat (Triticum aestivum L.) and barley (Hordeun vulgare L.), (3) high protein crops, such as peas (Pisum sativum L.) and beans (Vicia faba L.), (4) alfalfa (Medicago sativa L.), (5) woodlands and scrublands, including holly oak (Quercus ilex L.) and common retama (Retama sphaerocarpa L.), (6) urban soil, (7) olive groves (Olea europaea L.) and (8) burnt crop stubble. The best result was obtained using an expert classification algorithm, achieving a reliability rate of 95%. This result showed that the images of digital airborne sensors hold considerable promise for the future in the field of digital classifications because these images contain valuable information that takes advantage of the geometric viewpoint. Moreover, new classification techniques reduce problems encountered using high-resolution images; while reliabilities are achieved that are better than those achieved with traditional methods.https://sajs.co.za/article/view/10154digital aerial photographyexpert classification algorithmland-cover classificationobject- oriented classificationultracamd |
| spellingShingle | Alberto Perea José Meroño María Aguilera José de la Cruz Land-cover classification with an expert classification algorithm using digital aerial photographs South African Journal of Science digital aerial photography expert classification algorithm land-cover classification object- oriented classification ultracamd |
| title | Land-cover classification with an expert classification algorithm using digital aerial photographs |
| title_full | Land-cover classification with an expert classification algorithm using digital aerial photographs |
| title_fullStr | Land-cover classification with an expert classification algorithm using digital aerial photographs |
| title_full_unstemmed | Land-cover classification with an expert classification algorithm using digital aerial photographs |
| title_short | Land-cover classification with an expert classification algorithm using digital aerial photographs |
| title_sort | land cover classification with an expert classification algorithm using digital aerial photographs |
| topic | digital aerial photography expert classification algorithm land-cover classification object- oriented classification ultracamd |
| url | https://sajs.co.za/article/view/10154 |
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