Ranga Raju Vatsavai
Shashi Shekhar
ABSTRACT
Intelligent image information mining for thematic pattern extraction is a complex task. Ever increasing spatial, spectral, and temporal resolution poses several challenges to the geographic knowledge discovery community. Although the improvements in sensor technology and data collection methods may lead to improved geoinformation generation, it also places several constraints on data mining techniques. Moreover thematic classes are spectrally overlapping, that is, many thematic classes can not be separated by spectral features alone. In recent years we have developed several innovative machine learning approaches to address these problems. The resulting software system, called *Miner, was tested on several real world multisource spatiotemporal datasets. Experimental evaluation showed improved accuracy over conventional data mining approaches. In addition, we integrated *Miner with another popular open source machine learning system called Weka. In this demo we show the utility of *Miner for thematic information extraction from multisource spatiotemporal data (remote sensing images and ancillary geospatial databases).
- B. M. Kazar, S. Shekhar, D. J. Lilja, R. R. Vatsavai, and R. K. Pace. Comparing exact and approximate spatial auto-regression model solutions for spatial data analysis. In GIScience, pages 140--161, 2004.Google Scholar
Cross Ref
- S. Shekhar, P. Schrater, R. Vatsavai, W. Wu, and S. Chawla. Spatial contextual classification and prediction models for mining geospatial data. IEEE Transaction on Multimedia, 4(2):174--188, 2002. Google ScholarDigital Library
- R. R. Vatsavai and B. L. Bhaduri. A hybrid classification scheme for mining multisource geospatial data. In ICDM Workshop on Spatial and Spatiotemporal Data Mining, 2007. Google ScholarDigital Library
- R. R. Vatsavai, T. E. Burk, S. Shekhar, and M. Gini. An efficient hybrid classification system for mining multi-spectral remote sensing imagery guided by spatial databases. In 2nd Pattern Recognition of Remote Sensing Workshop, 2002.Google Scholar
- R. R. Vatsavai, S. Shekhar, and T. E. Burk. A semi-supervised learning method for remote sensing data mining. In ICTAI, pages 207--211, 2005. Google ScholarDigital Library
- R. R. Vatsavai, S. Shekhar, and T. E. Burk. An efficient spatial semi-supervised learning algorithm. Parallel Algorithms Appl., 22(6):427--437, 2007. Google ScholarDigital Library
- R. R. Vatsavai, S. Shekhar, T. E. Burk, and B. L. Bhaduri. *miner: A suit of classifiers for spatial, temporal, ancillary, and remote sensing data mining. In ITNG, 2008. Google ScholarDigital Library
- Weka. Weka machine learning project. http://www.cs.waikato.ac.nz/ml/index.html.Google Scholar
Index Terms
- *Miner: a spatial and spatiotemporal data mining system
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