Abstract
Aerial archaeology plays an important role in the detection and documentation of archaeological sites, which often cannot be easily seen from the ground. It is a quick way to survey large areas, but requires a lot of error-prone human work to analyze it afterwards. In this paper we utilize some of the best-performing image processing and data mining methods to develop a system capable of an accurate automated classification of such aerial photographs. The system consists of phases of image indexing, rough image segmentation, feature extraction, feature grouping and building the classifier. We present the results of experiments conducted on a real set of archaeological and non-archaeological aerial photographs and conclude with perspectives for future work.
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Kryszkiewicz, M., Skonieczny, Ł. (2006). Hierarchical Document Clustering Using Frequent Closed Sets. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33521-8_53
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DOI: https://doi.org/10.1007/3-540-33521-8_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33520-7
Online ISBN: 978-3-540-33521-4
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