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An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification*

An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification* Object-oriented image classification has tremendous potential to improve classification accuracies of land use and land cover (LULC), yet its benefits have only been minimally tested in peer-reviewed studies. We aim to quantify the benefits of an object-oriented method over a traditional pixel-based method for the mixed urban–suburban–agricultural landscape surrounding Gettysburg, Pennsylvania. To do so, we compared a traditional pixel-based classification using maximum likelihood to the object-oriented image classification paradigm embedded in eCognition Professional 4.0 software. This object-oriented paradigm has at least four components not typically used in pixel-based classification: (1) the segmentation procedure, (2) nearest neighbor classifier, (3) the integration of expert knowledge, and (4) feature space optimization. We evaluated each of these components individually to determine the source of any improvement in classification accuracy. We found that the combination of segmentation into image objects, the nearest neighbor classifier, and integration of expert knowledge yields substantially improved classification accuracy for the scene compared to a traditional pixel-based method. However, with the exception of feature space optimization, little or no improvement in classification accuracy is achieved by each of these strategies individually. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Professional Geographer Taylor & Francis

An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification*

The Professional Geographer , Volume 60 (1): 14 – Jan 1, 2008
14 pages

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References (28)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1467-9272
eISSN
0033-0124
DOI
10.1080/00330120701724152
Publisher site
See Article on Publisher Site

Abstract

Object-oriented image classification has tremendous potential to improve classification accuracies of land use and land cover (LULC), yet its benefits have only been minimally tested in peer-reviewed studies. We aim to quantify the benefits of an object-oriented method over a traditional pixel-based method for the mixed urban–suburban–agricultural landscape surrounding Gettysburg, Pennsylvania. To do so, we compared a traditional pixel-based classification using maximum likelihood to the object-oriented image classification paradigm embedded in eCognition Professional 4.0 software. This object-oriented paradigm has at least four components not typically used in pixel-based classification: (1) the segmentation procedure, (2) nearest neighbor classifier, (3) the integration of expert knowledge, and (4) feature space optimization. We evaluated each of these components individually to determine the source of any improvement in classification accuracy. We found that the combination of segmentation into image objects, the nearest neighbor classifier, and integration of expert knowledge yields substantially improved classification accuracy for the scene compared to a traditional pixel-based method. However, with the exception of feature space optimization, little or no improvement in classification accuracy is achieved by each of these strategies individually.

Journal

The Professional GeographerTaylor & Francis

Published: Jan 1, 2008

Keywords: image classification; land cover; land use; object-oriented; 图像分类; 土地覆盖; 土地利用; 面向对象; clasificación de imágenes; cobertura del suelo; uso del suelo; orientado a objetos

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