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Full hierarchic versus non-hierarchic classification approaches for mapping sealed surfaces at the rural-urban fringe using high-resolution satellite data

(2009) SENSORS. 9(1). p.22-45
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Abstract
Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this study two object-oriented approaches for high-resolution mapping of sealed surfaces are compared: a standard non-hierarchic approach and a full hierarchic approach using both multi-layer perceptrons and decision trees as learning algorithms. Both methods outperform the standard nearest neighbour classifier, which is used as a benchmark scenario. For the multi-layer perceptron approach, applying a hierarchic classification strategy substantially increases the accuracy of the classification. For the decision tree approach a one-against-all hierarchic classification strategy does not lead to an improvement of classification accuracy compared to the standard all-against-all approach. Best results are obtained with the hierarchic multi-layer perceptron classification strategy, producing a kappa value of 0.77. A simple shadow reclassification procedure based on characteristics of neighbouring objects further increases the kappa value to 0.84.
Keywords
REMOTELY-SENSED DATA, LAND-COVER CLASSIFICATIONS, NEURAL-NETWORK, SPATIAL-RESOLUTION, CONTEXTUAL INFORMATION, FEATURE-EXTRACTION, IKONOS, IMAGERY, TEXTURE, AREAS, SEGMENTATION, Urban mapping, sealed surfaces, hierarchic classification, multiple, layer perceptron, decision trees

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Citation

Please use this url to cite or link to this publication:

MLA
De Roeck, Tim, et al. “Full Hierarchic versus Non-Hierarchic Classification Approaches for Mapping Sealed Surfaces at the Rural-Urban Fringe Using High-Resolution Satellite Data.” SENSORS, vol. 9, no. 1, 2009, pp. 22–45.
APA
De Roeck, T., Van de Voorde, T., & Canters, F. (2009). Full hierarchic versus non-hierarchic classification approaches for mapping sealed surfaces at the rural-urban fringe using high-resolution satellite data. SENSORS, 9(1), 22–45.
Chicago author-date
De Roeck, Tim, Tim Van de Voorde, and Frank Canters. 2009. “Full Hierarchic versus Non-Hierarchic Classification Approaches for Mapping Sealed Surfaces at the Rural-Urban Fringe Using High-Resolution Satellite Data.” SENSORS 9 (1): 22–45.
Chicago author-date (all authors)
De Roeck, Tim, Tim Van de Voorde, and Frank Canters. 2009. “Full Hierarchic versus Non-Hierarchic Classification Approaches for Mapping Sealed Surfaces at the Rural-Urban Fringe Using High-Resolution Satellite Data.” SENSORS 9 (1): 22–45.
Vancouver
1.
De Roeck T, Van de Voorde T, Canters F. Full hierarchic versus non-hierarchic classification approaches for mapping sealed surfaces at the rural-urban fringe using high-resolution satellite data. SENSORS. 2009;9(1):22–45.
IEEE
[1]
T. De Roeck, T. Van de Voorde, and F. Canters, “Full hierarchic versus non-hierarchic classification approaches for mapping sealed surfaces at the rural-urban fringe using high-resolution satellite data,” SENSORS, vol. 9, no. 1, pp. 22–45, 2009.
@article{8645252,
  abstract     = {Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this study two object-oriented approaches for high-resolution mapping of sealed surfaces are compared: a standard non-hierarchic approach and a full hierarchic approach using both multi-layer perceptrons and decision trees as learning algorithms. Both methods outperform the standard nearest neighbour classifier, which is used as a benchmark scenario. For the multi-layer perceptron approach, applying a hierarchic classification strategy substantially increases the accuracy of the classification. For the decision tree approach a one-against-all hierarchic classification strategy does not lead to an improvement of classification accuracy compared to the standard all-against-all approach. Best results are obtained with the hierarchic multi-layer perceptron classification strategy, producing a kappa value of 0.77. A simple shadow reclassification procedure based on characteristics of neighbouring objects further increases the kappa value to 0.84.},
  author       = {De Roeck, Tim and Van de Voorde, Tim and Canters, Frank},
  issn         = {1424-8220},
  journal      = {SENSORS},
  keywords     = {REMOTELY-SENSED DATA,LAND-COVER CLASSIFICATIONS,NEURAL-NETWORK,SPATIAL-RESOLUTION,CONTEXTUAL INFORMATION,FEATURE-EXTRACTION,IKONOS,IMAGERY,TEXTURE,AREAS,SEGMENTATION,Urban mapping,sealed surfaces,hierarchic classification,multiple,layer perceptron,decision trees},
  language     = {eng},
  number       = {1},
  pages        = {22--45},
  title        = {Full hierarchic versus non-hierarchic classification approaches for mapping sealed surfaces at the rural-urban fringe using high-resolution satellite data},
  url          = {http://dx.doi.org/10.3390/s90100022},
  volume       = {9},
  year         = {2009},
}

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