office of sa to cns geointelligence 2009. introduction data mining vs image mining image mining -...
TRANSCRIPT
Office of SA to CNSGeoIntelligence 2009
Introduction
Data Mining vs Image Mining
Image Mining - Issues and Challenges
CBIR
Image Mining Process
Ontology for Image Mining
Conclusion
GeoIntelligence 2009 Office of SA to CNS
An Overview of Image Mining
Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images.
Image mining is more than just an extension of data mining to image domain
Military Applications:Mobility Analysis
Traffficability Analysis
Potential Corridor of Landing
Image Mining
GeoIntelligence 2009 Office of SA to CNS
Office of SA to CNSGeoIntelligence 2009
The features may include: Color (in various channels), Texture (e.g. Directionality,
likeliness, contrast, roughness and coarseness), edge,
luminance, shape, spatial relations, temporal
information, statistical measures (e.g. moments – mean, variance, standard deviation, skewness etc).
Data mining searches: Valid patternsPreviously unknown patternsPotentially useful patternsUnderstandable patterns
Image mining extracts:Strategic informationRelationships and patternsLandscape aspects
Challenges (image mining)Relative valuesSpatial informationMultiple interpretationPatterns representation
GeoIntelligence 2009 Office of SA to CNS
Image information mining is an interdisciplinary endeavor Computer vision (image processing) Pattern recognition (classification & clustering) Databases (images & ancillary data) Information Retrieval (indexing and queries)
Challenges of mining information in remote sensing images Multi /hyper spectral (huge size, different formats) Variability of data sets (formats, types and structures) Time consuming preprocessing (correction and registration) Complex spatial / temporal associations Feature extraction & semantic definition (application specific) Ancillary data (climate variables, digital elevation model) Interpretation (a-priori and domain knowledge)
GeoIntelligence 2009 Office of SA to CNS
GeoIntelligence 2009 Office of SA to CNS
Content-based image retrieval (CBIR)
Modeling the contents of the image as a set of attributes
Using an integrated feature-extraction/object-recognition system
Image Mining Process
Image Databa
se
Pre-processing
Transformation & Feature Extraction
Mining
Interpretation & Evaluation
Knowledge
Office of SA to CNSGeoIntelligence 2009
Graph Mining Approach
Attribute Relational Graph (ARG)
Regional Adjacency Graph (RAG)
Ontological Approach
Image Mining Approaches
GeoIntelligence 2009 Office of SA to CNS
Method Ontology
Structural
Ontology
Physical Ontology
Semantic
Mediator
Application
Ontology
Task Ontology
Ontology for Image Mining
Incorporation of semantic information into the knowledge discovery process
Ontology describes a particular reality with a specific vocabulary, using a set of hypothesis related to the intentional meaning of the words in this vocabulary
Physical ontology
Structural ontology
Method Ontology
Office of SA to CNSGeoIntelligence 2009
Image Mining Systems
GeoMiner
A spatial data mining system developed by Han et al (1997)
ADaM
A NASA-developed Image Mining System
MSIM
A Multi-sensor Image Mining System developed by BAE Systems
GeoIntelligence 2009 Office of SA to CNS
GeoIntelligence 2009 Office of SA to CNS
Conclusion
Currently, most image processing techniques are designed to operate on a single image
Very few techniques for image data mining and information extraction in large image data sets
“Knowledge gap” in the process of deriving information from images and digital maps
Future research directions in remote sensing image mining include tracking individual trajectories of change
GeoIntelligence 2009 Office of SA to CNS
QUESTIONS
?
GeoIntelligence 2009 Office of SA to CNS
SA to CNS
Satellite Data
TERRAIN DATABASE MANAGEMENT SYSTEM (TDMS)
TDSSTerrain Decision Support System
Attribute Data, Spatial Data, & Knowledge Base
Spatial DB Server (GIS)
R DB Server (RDBMS)
Relational DB Miner
Spatial/ImageDB MinerTKDD
Terrain Knowledge Discovery from Data
Format Converter
TD&CS
VLDHVery Large
Database Handler
Military Applications
Interactive Mining I/F
Application Interface
DTDB
Terrain Analysis and Visualization
Training Set, Testing & Validation Data Set
MGD
TRMS
TPMS
Map Data
DATA INPUT
Expert Refinement
Discovered Rules/ Features (Natural & Manmade)
ADSApplication
Development System
Knowledge Acquisition I/F
Domain Expert
Knowledge Base
Knowledge compiler
GIS Mapping I/F
Inference Mechanism
PCLNMOCCM
Field Data
Scale Converter
Projection Converter
DTSData Transformation System
GIS Mapping I/F
DTRL
CCM-Cross Country Mobility
NMO-Natural & Manmade Obstacles
PCL-Potential Corridor of Landing
TD&CS-Troops Deployment & Camping Sites
LOS-Line of Sight
LOS