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