A VECTOR AGENT APPROACH EXTRACT … · A B Based on slide by Jarlath O’Neil Dunne by Austin Troy and Weiqi Zhou, 2008 1.2. Methods The limitations of the pixel –based classification

A VECTOR AGENT APPROACH TO EXTRACT THE BOUNDARIES OF

REAL-WORLD PHENOMENA FROM SATELLITE IMAGES

Kambiz Borna Antoni Moore amp Pascal Sirguey

School of Surveying

University of Otago Dunedin New Zealand

11 Image Classification

What is the image classification

12 Methods

Supervised Classification Unsupervised Classification

A Pixel-based

based on spectral reflectance

A B

Based on slide by Jarlath OrsquoNeil Dunne

by Austin Troy and Weiqi Zhou 2008

12 Methods

The limitations of the pixel ndashbased classification

12 Methods

B Object-based classification

based on image object

Image-objects are groups of connected pixels that are supposed

to depict a homogeneous thematic meaning

B

C



12 Methods

n

lcpt compactness

12 OBC Process

Object-based image classification process

Image segmentation Image Classification

The objects remain unchanged once they are created

Image objects have no direct relationship to real-world objects

That means

13 Limitations

we have a correct extraction and shaping of interest objects

may

131 Proposed Method

Moore et al (2011)

They are objects which can support a dynamic and irregular geometry

Geographical Vector Agents (VA)

3 1 Image Object Geometry in The

Context of The Vector Agent

Image object is automatically formed as a point in the pixel centre

A new point along four cardinal directions by a constant distance that is specified

by cell size

Rules

32 Image Objects Construction

and Evolution

4 Implementation

Based on a synthetic image

the growing process of two agent without negotiation

4 Implementation

Case1 negotiation between two vector agents including shrinking and growing process

4 Implementation

Case 2 negotiation between two vector agents including splitting process

4 Implementation

Case 3 negotiation between two vector agents including joining process

4 Implementation

Case 4 VAs are the goal oriented objects

Here they are initially defined to find the water and shadow

Water object eventually appears to be less likely than

shadow and VA merges under a single shadow object

Ikonos satellite image

4 Implementation

Case 5 VAs can use ancillary layer

Eg the sample regions including

ldquoardquo rdquobrdquo and rdquocrdquo have similar spectral

reflectance yet they have different

elevations

The same spectral reflectance

but in different level based on

a DEM layer

a

b

c

This research has highlighted some abilities of the VA to support a dynamic

geometry to image classification

41 Summary

Thank you for your attention

References

Baatz M and Schaumlpe A (2000) ldquoMultiresolution segmentation an optimization approach for high quality multiscale image segmentationrdquo In

Strobl J Blaschke T (Eds) Angewandte Geogr Informationsverarbeitung vol XII WichmannHeidelbergpp 12ndash 23

Baatz M Hoffmann C and Willhauck G (2007) rdquo Object-Based Image Analysis Spatial Concepts for Knowledge-Driven Remote Sensingrdquo

Springer pp29-42

Benenson I and Torrens P (2004) rdquo Geosimulation Automata-Based Modelling of Urban Phenomena Englandrdquo Wiley

Benz UC Hofmann PWillhauck G Lingenfelder I and Heynen M(2004) ldquoMulti-resolution object-oriented fuzzy analysis of remote sensing

data for GIS-ready informationrdquo ISPRS J Photogrammetry Remote Sensing 58 239ndash258Carlos M Fonseca and Peter J Fleming (1995)

Evolutionary Computation Springer

Bian L (1997)rdquo Multiscale Nature of Spatial Data in Scaling up Environmental Modelsrdquo in Scale in Remote Sensing and GIS Quattrochi DA and

Goodchild MF(Eds) Lewis pp13-27

Hay J Blashke TMarceau J and Bouchard A(2003) ldquoA comparison of three imageobject methods for the multiscale analysis of landscape

structurerdquo ISPRS Journal of Photogrammetry amp Remote Sensing 57 pp 327ndash345

Hay GJ Castilla G Wulder MA and Ruiz JR(2005) ldquoAn automated object-based approach for the multiscale image segmentation of forest

scenesrdquo International Journal of Applied Earth Observation and Geoinformation 7 pp 339ndash359

Hammam Y Moore A and Whigham P(2007) The dynamic geometry of Geographical Vector AgentsComputers Environment and Urban

Systems vol31 no5 pp 502-519

Gao J (2009)rdquoDigital Analysis of Remotely Sensed Imageryrdquo McGrow-Hill pp 434-435

Goodchild M(2001) ldquoIssues in spatially explicit modellingrdquo In D Parker T Berger amp S M Manson(Eds) Agent-based models of land-use and land-

cover change (pp 13ndash17) Irvine

Manson SM Sun S and Bonsal D(2012)rdquo Agent-Based Models of Geographical Systemsrdquo Springerpp125-141

Manson S M(2007)rdquo Does scale exist An epistemological scale continuum for complex humanndashenvironment systemsrdquo Geoforum Accepted in

press

Moore A(2011) ldquoGeographical Vector Agent Based Simulation for Agricultural Land Use Modellingrdquo in Advanced GeoSimulation Models

MarceauD and Benenson I (Eds) pp30-48

MacEachren A M Robinson A Hopper S Gardner S Murray R Gahegan M and Hetzler E(2005) ldquoVisualizing Geospatial Information

Uncertainty What We Know and What We Need to Know ldquo Cartography and Geographic Information Science Vol 32 No 3 2005 pp 139-160

Rouff CA Hinchey M Rash J Truszkowski W and GordonS D (Eds) (2006)rdquo Agent Technology from a Formal Perspectiverdquo Springer

Tian J and Chen DM (2007) ldquoInternational Journal of Remote Sensingrdquo ISSN 0143-1161 printISSN 1366-5901 online Taylor amp Francis

Torrens P and Benenson I(2003) Geographic Automata Systems International Journal of Geographic Information Science vol 10 no4 pp385-

412

Walsh S J Moody A Allen T R and Brown DG(1997) ldquoScale Dependence of NDVI and Its Relationship to Mountainous Terrainrdquo in Scale in

Remote Sensing and GIS Quattrochi DA and Goodchild MF(Eds) Lewis pp27-57

Yuan M Goodchild M F Cova T J (2007) ldquoTowards a General Theory of Geographic Representation in GISrdquo International Journal of

Geographic Information Science Volume 21 Pages 239-260

11 Image Classification

What is the image classification

12 Methods


A Pixel-based


A B



12 Methods


12 Methods





B

C



12 Methods

n

lcpt compactness

12 OBC Process





That means

13 Limitations


may

131 Proposed Method

Moore et al (2011)







by cell size

Rules


and Evolution

4 Implementation



4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





12 Methods


A Pixel-based


A B



12 Methods


12 Methods





B

C



12 Methods

n

lcpt compactness

12 OBC Process





That means

13 Limitations


may

131 Proposed Method

Moore et al (2011)







by cell size

Rules


and Evolution

4 Implementation



4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





A B



12 Methods


12 Methods





B

C



12 Methods

n

lcpt compactness

12 OBC Process





That means

13 Limitations


may

131 Proposed Method

Moore et al (2011)







by cell size

Rules


and Evolution

4 Implementation



4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





12 Methods





B

C



12 Methods

n

lcpt compactness

12 OBC Process





That means

13 Limitations


may

131 Proposed Method

Moore et al (2011)







by cell size

Rules


and Evolution

4 Implementation



4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





B

C



12 Methods

n

lcpt compactness

12 OBC Process





That means

13 Limitations


may

131 Proposed Method

Moore et al (2011)







by cell size

Rules


and Evolution

4 Implementation



4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





12 OBC Process





That means

13 Limitations


may

131 Proposed Method

Moore et al (2011)







by cell size

Rules


and Evolution

4 Implementation



4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





13 Limitations


may

131 Proposed Method

Moore et al (2011)







by cell size

Rules


and Evolution

4 Implementation



4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





131 Proposed Method

Moore et al (2011)







by cell size

Rules


and Evolution

4 Implementation



4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412









by cell size

Rules


and Evolution

4 Implementation



4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412







by cell size

Rules


and Evolution

4 Implementation



4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





4 Implementation



4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





4 Implementation


4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





4 Implementation


4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





4 Implementation


4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





4 Implementation






4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412





4 Implementation





elevations



a DEM layer

a

b

c



41 Summary


References




Springer pp29-42


















press








412







41 Summary


References




Springer pp29-42


















press








412





References




Springer pp29-42


















press








412





Documents

A VECTOR AGENT APPROACH EXTRACT … · A B Based on slide by Jarlath O’Neil Dunne by Austin Troy and Weiqi Zhou, 2008 1.2. Methods The limitations of the pixel –based classification