Empirical Inference Conference Paper 2008

A Novel Protocol for Accuracy Assessment in Classification of Very High Resolution Multispectral and SAR Images

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Empirical Inference

This paper presents a novel protocol for the accuracy assessment of thematic maps obtained by the classification of very high resolution images. As the thematic accuracy alone is not sufficient to adequately characterize the geometrical properties of classification maps, we propose a novel protocol that is based on the analysis of two families of indexes: (i) the traditional thematic accuracy indexes, and (ii) a set of geometric indexes that characterize different geometric properties of the objects recognized in the map. These indexes can be used in the training phase of a classifier for identifying the parameters values that optimize classification results on the basis of a multi-objective criterion. Experimental results obtained on Quickbird images show the effectiveness of the proposed protocol in selecting classification maps characterized by better tradeoff between thematic and geometric accuracy with respect to standard accuracy measures.

Author(s): Bruzzone, L. and Persello, C.
Links:
Pages: II-265-II-268
Year: 2008
Month: July
Day: 0
Publisher: IEEE
Bibtex Type: Conference Paper (inproceedings)
Address: Piscataway, NJ, USA
DOI: 10.1109/IGARSS.2008.4778978
Event Name: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2008)
Event Place: Boston, MA , USA
Digital: 0
Electronic Archiving: grant_archive
ISBN: 978-1-4244-2807-6

BibTex

@inproceedings{BruzzoneP2008,
  title = {A Novel Protocol for Accuracy Assessment in Classification of Very High Resolution Multispectral and SAR Images },
  abstract = {This paper presents a novel protocol for the accuracy assessment of thematic maps obtained by the classification of very high resolution images. As the thematic accuracy alone is not sufficient to adequately characterize the geometrical properties of classification maps, we propose a novel protocol that is based on the analysis of two families of indexes: (i) the traditional thematic accuracy indexes, and (ii) a set of geometric indexes that characterize different geometric properties of the objects recognized in the map. These indexes can be used in the training phase of a classifier for identifying the parameters values that optimize classification results on the basis of a multi-objective criterion. Experimental results obtained on Quickbird images show the effectiveness of the proposed protocol in selecting classification maps characterized by better tradeoff between thematic and geometric accuracy with respect to standard accuracy measures.},
  pages = {II-265-II-268 },
  publisher = {IEEE},
  address = {Piscataway, NJ, USA},
  month = jul,
  year = {2008},
  slug = {bruzzonep2008},
  author = {Bruzzone, L. and Persello, C.},
  month_numeric = {7}
}