Computational Sciences & Engineering Division Geographic Information Science and Technology Landsat LIDAR data Hi-res satellite imagery sensor networks large national datasets (LIDAR, HSIP) NOAA Cluster analysis - group sets of objects into clusters using established statistical methods in order to identify interesting distributions and patterns in the data Given an image and training samples, the objective is to partition the data into similar groups. Problem Solution Distinguishing ecological regions Determining soil types Mapping forests Identifying crop patterns Identifying soil quality Determining water quality Environmental management Resource management Climate Changes and Imapcts Landsat ETM – FCC Image Classification with iid assumption Spatial classification Clustering The process of grouping a set of data objects into clusters such that intra-cluster similarity is high and inter-cluster similarity is low. Clustering Algorithm Using R Statistical Interface and KMeans, random center points are created for each cluster. Data points are assigned to the cluster based on the nearest center point. The center point of each cluster is recalculated based on the average of all data points in the cluster. The centers and cluster size may change several times. After several iterations, distinct and statistically sound clusters are created that can be used to identify patterns in the data. Ecological Regions Soil Type Uses Geographical Databases Remote sensing makes use of visible, near infrared and short-wave infrared sensors to form images of the earth's surface by detecting the solar radiation reflected from targets on the ground. Different materials reflect and absorb differently at different wavelengths. Ranga Raju Vatsavai, Budhendra L. Bhaduri, Eddie Bright, Nagendra Singh, Goo Jun and Joydeep Ghosh (2009). Poster: Land Use and Land Cover Classification. Prepared by Oak Ridge National Laboratory for the U. S. Department of Energy. Research supported through LDRD program. Ranga Raju Vatsavai, Budhendra L. Bhaduri, Shashi Shekhar and Thomas E. Burk (2009). Poster: Miner: A Spatial and Spatiotemporal Data Mining System. Prepared by Oak Ridge National Laboratory for the U. S. Department of Energy. Ranga Raju Vatsavai (2010). Presentation: Introduction to spatial data mining. Oak Ridge National Laboratory. Acknowledgment Prepared by Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee , managed by UT-Battelle, LLC for the U. S. Department of Energy under contract no. DEAC05-00OR References and Acknowledgements Remote Sensing Each data point is compared to each center of each cluster. Which ever center point is closest to the data point, that is the cluster the data point is moved to. Some data points may be in the correct cluster, some may have to be changed. Once the clusters have been developed and there is more intra- class similarity than inter-class similarity, the data can then be graphed to show the cluster locations. Cluster Plot Shelly Turner ACTS Teacher Raju Vatsavai Mentor Budhendra L. Bhaduri Group Leader Art Stewart ORISE Advisor