Browsing by Subject "Data source"
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Item Open Access Land cover classification with multi-sensor fusion of partly missing data(American Society for Photogrammetry and Remote Sensing, 2009-05) Aksoy, S.; Koperski, K.; Tusk, C.; Marchisio, G.We describe a system that uses decision tree-based tools for seamless acquisition of knowledge for classification of remotely sensed imagery. We concentrate on three important problems in this process: information fusion, model understandability, and handling of missing data. Importance of multi-sensor information fusion and the use of decision tree classifiers for such problems have been well-studied in the literature. However, these studies have been limited to the cases where all data sources have a full coverage for the scene under consideration. Our contribution in this paper is to show how decision tree classifiers can be learned with alternative (surrogate) decision nodes and result in models that are capable of dealing with missing data during both training and classification to handle cases where one or more measurements do not exist for some locations. We present detailed performance evaluation regarding the effectiveness of these classifiers for information fusion and feature selection, and study three different methods for handling missing data in comparative experiments. The results show that surrogate decisions incorporated into decision tree classifiers provide powerful models for fusing information from different data layers while being robust to missing data. © 2009 American Society for Photogrammetry and Remote Sensing.Item Open Access Segmenting and labeling query sequences in a multidatabase environment(Springer, Berlin, Heidelberg, 2011) Acar, Aybar C.; Motro, A.When gathering information from multiple independent data sources, users will generally pose a sequence of queries to each source, combine (union) or cross-reference (join) the results in order to obtain the information they need. Furthermore, when gathering information, there is a fair bit of trial and error involved, where queries are recursively refined according to the results of a previous query in the sequence. From the point of view of an outside observer, the aim of such a sequence of queries may not be immediately obvious. We investigate the problem of isolating and characterizing subsequences representing coherent information retrieval goals out of a sequence of queries sent by a user to different data sources over a period of time. The problem has two sub-problems: segmenting the sequence into subsequences, each representing a discrete goal; and labeling each query in these subsequences according to how they contribute to the goal. We propose a method in which a discriminative probabilistic model (a Conditional Random Field) is trained with pre-labeled sequences. We have tested the accuracy with which such a model can infer labels and segmentation on novel sequences. Results show that the approach is very accurate (> 95% accuracy) when there are no spurious queries in the sequence and moderately accurate even in the presence of substantial noise (∼70% accuracy when 15% of queries in the sequence are spurious). © 2011 Springer-Verlag.