Identification of lacunae in a wood painting: comparative analysis of classification methods in GIS-based spatial analysis
Main Article Content
Abstract
In this text some GIS-based spatial analysis methods are presented, enabling the quantification of areas with white lacunae in a 16th century wood painting. With an imageproduced during a conservation/restoration intervention, and applying a principal component analysis (PCA) as well as several classificators, thematic maps of the lacunae were created. Four methods were used: automatic division of histogram in intervals (level slicing); alimiarization (thresholding); a supervised classification method based in the productionof training areas, a spectral signature and a maximum likelihood classification; and anunsupervised classification method with a cell aggregation algorithm (iso cluster). In the conditions under which essays were made, best results in the identification of lacunae were obtained with the limiarization (thresholding) and the supervised classification methods.
Downloads
References
CHOU, Yue-Hon. Exploring Spatial Analysis in Geographic Information Systems. Albany, Nova Iorque: On World Press, 1997.
ESRI, a. «Principal Components». In ArcGIS Help 9.3. 2008 [consulta: 30.12. 2009]. http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=Principal_Components
ESRI, b. « MLClassify». In ArcGIS Help 9.3. 2008 [consulta: 30.12. 2009]. http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=MLClassify
ESRI, c. «Iso Cluster». In ArcGIS Help 9.3. 2008 [consulta: 30.12.2009]. http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=welcome
LILLESAND, Thomas; KIEFER, Ralph; CHIPMAN, Jonathan. Remote Sensing and Image Interpretation. 6.ª ed. Hoboken, New Jersey: John Wiley& Sons, 2008.
MARK, Robert; BILLO, Evelyn. Computer-Assisted Photographic Documentation of Rock Art. In: Coalition, 11 (2006), pp. 10-14.