A Semi-Supervised Learning Method for Remote Sensing Data Mining
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
A Hybrid Classification Scheme for Mining Multisource Geospatial Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
An efficient spatial semi-supervised learning algorithm
International Journal of Parallel, Emergent and Distributed Systems
*Miner: A Suit of Classifiers for Spatial, Temporal, Ancillary, and Remote Sensing Data Mining
ITNG '08 Proceedings of the Fifth International Conference on Information Technology: New Generations
Spatial contextual classification and prediction models for mining geospatial data
IEEE Transactions on Multimedia
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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).