Pedosphere 23(3): 290--297, 2013
ISSN 1002-0160/CN 32-1315/P
©2013 Soil Science Society of China
Published by Elsevier B.V. and Science Press
| Object-based method outperforms per-pixel method for land cover classification in a protected area of the Brazilian Atlantic rainforest region |
| T. RITTL1,2, M. COOPER2, R. J. HECK3 and M. V. R. BALLESTER4 |
1 Department of Soil Quality, University of Wageningen, Wageningen 6700 AA (Netherlands)
2Department of Soil Science, University of São Paulo, Piracicaba SP13418-900 (Brazil)
3School of Environmental Sciences, University of Guelph, Guelph ON N1G2W1 (Canada)
4
Center for Nuclear Energy in Agriculture (CENA), University of São Paulo, Piracicaba SP 13416-000 (Brazil) |
| ABSTRACT |
| Conventional image classification based on pixels hinders the possibilities to obtain information contained in images, while modern object-based classification methods increase the acquisition of information about the object and the context in which it is inserted in the image. The objective of this study was to investigate the performance of different classification methods for land cover mapping in the vicinity of the Alto Ribeira Tourist State Park, a Brazilian Atlantic rainforest area. Two classification methods were tested, including i) a hybrid per-pixel classification using the image processing software ERDAS Imagine version 9.1 and ii) an object-based classification using the software eCognition version 5. In the first method, six different classes were established, while in the second method, another two classes were established in addition to the six classes in the first method. Accuracy assessment of the classification results presented showed that the object-based classification with a Kappa index value of 0.8687 outperformed the per-pixel classification with a Kappa index value of 0.2224. Application of the user’s knowledge during the object-based classification process achieved the desired quality; therefore, the use of inter-relationships between objects, superclasses, subclasses, and neighboring classes were critical to improving the efficiency of land cover classification. |
| Key Words: accuracy assessment, image classification, Kappa index, user’s knowledge |
| Citation: Rittl, T., Cooper, M., Heck, R. J., Ballester, V. R., Cooper, M., Heck, R. J. and Ballester, V. R. 2013. Object-based method outperforms per-pixel method for land cover classification in a protected area of the Brazilian Atlantic rainforest region. Pedosphere. 23(3): 290-297. |
|
View Full Text
|
|
|
|
