TY - GEN
T1 - *Miner
T2 - 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM GIS 2008
AU - Vatsavai, Ranga Raju
AU - Shekhar, Shashi
AU - Burk, Thomas E.
AU - Bhaduri, Budhendra
PY - 2008
Y1 - 2008
N2 - 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).
AB - 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).
KW - EM
KW - GMM
KW - Multisource data
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=70449711372&partnerID=8YFLogxK
U2 - 10.1145/1463434.1463532
DO - 10.1145/1463434.1463532
M3 - Conference contribution
AN - SCOPUS:70449711372
SN - 9781605583235
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 531
EP - 532
BT - Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM GIS 2008
Y2 - 5 November 2008 through 7 November 2008
ER -