Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 439113
Title Object-based method outperforms per-pixel method for land cover classification in a protected area of the Brazilian Atlantic rainforest region
Author(s) Francischinelli Rittl, T.; Cooper, M.; Heck, R.J.; Ballester, V.R.
Source Pedosphere 23 (2013)3. - ISSN 1002-0160 - p. 290 - 297.
DOI http://dx.doi.org/10.1016/S1002-0160(13)60018-1
Department(s) Forest and Nature Conservation Policy
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2013
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
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