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An assessment of geometric activity features for per-pixel classification of urban man-made objects using very high resolution satellite imagery

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Abstract
In this paper, we propose the use of Geometric Activity (GA) features for detecting man-made objects in urban areas using VHR satellite imagery. These features describe the geometric context of a pixel without the necessity of segmentation and can be integrated as extra bands in a per-pixel classification. Two main types of GA features were investigated: ridge features based on the well-known facet model and morphological features obtained by applying closing transforms with structuring elements of different size and shape. Our findings show a substantial increase in classification accuracy for the man-made object classes "roads and buildings with dark roof" after inclusion of GA features. Next to GA features, the use of object-based features derived from eCognition(R), containing both geometric and textural information, was also investigated for per-pixel classification. Accuracies obtained with object-based features are comparable to the accuracies obtained with GA features. The inclusion of both GA features and object-based features further improves the overall accuracy. GA features and object-based features thus contain complementary information.
Keywords
MACHINE LEARNING ALGORITHMS, LAND-COVER CLASSIFICATION, AUTOMATIC ROAD EXTRACTION, REMOTE-SENSING IMAGES, MULTISPECTRAL IMAGERY, SPATIAL-RESOLUTION, TEXTURE, MULTISCALE, MODEL

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MLA
Chan, Jonathan Cheung-Wai et al. “An Assessment of Geometric Activity Features for Per-pixel Classification of Urban Man-made Objects Using Very High Resolution Satellite Imagery.” PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 75.4 (2009): 397–411. Print.
APA
Chan, J. C.-W., Bellens, R., Canters, F., & Gautama, S. (2009). An assessment of geometric activity features for per-pixel classification of urban man-made objects using very high resolution satellite imagery. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 75(4), 397–411.
Chicago author-date
Chan, Jonathan Cheung-Wai, Rik Bellens, Frank Canters, and Sidharta Gautama. 2009. “An Assessment of Geometric Activity Features for Per-pixel Classification of Urban Man-made Objects Using Very High Resolution Satellite Imagery.” Photogrammetric Engineering and Remote Sensing 75 (4): 397–411.
Chicago author-date (all authors)
Chan, Jonathan Cheung-Wai, Rik Bellens, Frank Canters, and Sidharta Gautama. 2009. “An Assessment of Geometric Activity Features for Per-pixel Classification of Urban Man-made Objects Using Very High Resolution Satellite Imagery.” Photogrammetric Engineering and Remote Sensing 75 (4): 397–411.
Vancouver
1.
Chan JC-W, Bellens R, Canters F, Gautama S. An assessment of geometric activity features for per-pixel classification of urban man-made objects using very high resolution satellite imagery. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING. American Society for Photogrammetry and remote sensing; 2009;75(4):397–411.
IEEE
[1]
J. C.-W. Chan, R. Bellens, F. Canters, and S. Gautama, “An assessment of geometric activity features for per-pixel classification of urban man-made objects using very high resolution satellite imagery,” PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, vol. 75, no. 4, pp. 397–411, 2009.
@article{539526,
  abstract     = {{In this paper, we propose the use of Geometric Activity (GA) features for detecting man-made objects in urban areas using VHR satellite imagery. These features describe the geometric context of a pixel without the necessity of segmentation and can be integrated as extra bands in a per-pixel classification. Two main types of GA features were investigated: ridge features based on the well-known facet model and morphological features obtained by applying closing transforms with structuring elements of different size and shape. Our findings show a substantial increase in classification accuracy for the man-made object classes "roads and buildings with dark roof" after inclusion of GA features. Next to GA features, the use of object-based features derived from eCognition(R), containing both geometric and textural information, was also investigated for per-pixel classification. Accuracies obtained with object-based features are comparable to the accuracies obtained with GA features. The inclusion of both GA features and object-based features further improves the overall accuracy. GA features and object-based features thus contain complementary information.}},
  author       = {{Chan, Jonathan Cheung-Wai and Bellens, Rik and Canters, Frank and Gautama, Sidharta}},
  issn         = {{0099-1112}},
  journal      = {{PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING}},
  keywords     = {{MACHINE LEARNING ALGORITHMS,LAND-COVER CLASSIFICATION,AUTOMATIC ROAD EXTRACTION,REMOTE-SENSING IMAGES,MULTISPECTRAL IMAGERY,SPATIAL-RESOLUTION,TEXTURE,MULTISCALE,MODEL}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{397--411}},
  publisher    = {{American Society for Photogrammetry and remote sensing}},
  title        = {{An assessment of geometric activity features for per-pixel classification of urban man-made objects using very high resolution satellite imagery}},
  url          = {{http://dx.doi.org/10.14358/PERS.75.4.397}},
  volume       = {{75}},
  year         = {{2009}},
}

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