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Transcript of Computational Sciences & Engineering Division Geographic Information Science and Technology Landsat...
Computational Sciences & Engineering Division
Geographic Information Science and Technology
LandsatLIDAR data
Hi-res satellite imagerysensor 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
Bare
Crop
Grass
Upland Conifer
Upland Hardwood
Water
Wetlands
High Density Urban
Low Density Urban
Lowland Conifer
Lowland Hardwood
Solution
Distinguishing ecological regionsDetermining soil types
Mapping forestsIdentifying crop patterns
Identifying soil qualityDetermining water quality
Environmental managementResource 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 AlgorithmUsing 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 37831-6285, managed by UT-Battelle, LLC for the U. S. Department of Energy under contract no. DEAC05-00OR22725.
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 TurnerACTS Teacher
Raju VatsavaiMentor
Budhendra L. BhaduriGroup Leader
Art StewartORISE Advisor
GIST