A VECTOR AGENT APPROACH TO EXTRACT THE BOUNDARIES OF REAL-WORLD PHENOMENA FROM SATELLITE IMAGES

Transcription

1 A VECTOR AGENT APPROACH TO EXTRACT THE BOUNDARIES OF REAL-WORLD PHENOMENA FROM SATELLITE IMAGES Kambiz Borna, Antoni Moore & Pascal Sirguey School of Surveying University of Otago. Dunedin, New Zealand

2 1.1. Image Classification What is the image classification?

3 1.2. Methods A. Pixel-based: based on spectral reflectance Supervised Classification Unsupervised Classification

4 1.2. Methods The limitations of the pixel based classification A B Based on slide by Jarlath O Neil Dunne by Austin Troy and Weiqi Zhou, 2008

5 1.2. 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.

6 1.2. Methods B C compactness cpt l n Based on slide by Jarlath O Neil Dunne by Austin Troy and Weiqi Zhou, 2008

7 1.2. OBC Process Object-based image classification process Image segmentation Image Classification That means The objects remain unchanged once they are created Image objects have no direct relationship to real-world objects

8 1.3. Limitations may we have a correct extraction and shaping of interest objects

9 Proposed Method They are objects which can support a dynamic and irregular geometry: Geographical Vector Agents (VA) Moore et al. (2011)

10 3. 1. Image Object Geometry in The Context of The Vector Agent

11 3.2. Image Objects Construction and Evolution Rules 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

12 4. Implementation Based on a synthetic image : the growing process of two agent without negotiation

13 4. Implementation Case1: negotiation between two vector agents including shrinking and growing process.

14 4. Implementation Case 2: negotiation between two vector agents including splitting process.

15 4. Implementation Case 3: negotiation between two vector agents including joining process.

16 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

17 4. Implementation a b c Case 5: VAs can use ancillary layer. E.g., the sample regions including a, b and c have similar spectral reflectance, yet they have different elevations. The same spectral reflectance but in different level based on a DEM layer

18 4.1. Summary This research has highlighted some abilities of the VA to support a dynamic geometry to image classification. Thank you for your attention

19 References: Baatz, M. and Scha pe, A. (2000). Multiresolution segmentation: an optimization approach for high quality multiscale image segmentation. In: Strobl, J., Blaschke, T. (Eds.), Angewandte Geogr. Informationsverarbeitung, vol. XII. Wichmann,Heidelberg,pp Baatz, M., Hoffmann, C. and Willhauck, G. (2007). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing, Springer, pp Benenson, I. and Torrens, P. (2004). Geosimulation Automata-Based Modelling of Urban Phenomena. England, Wiley. Benz, U.C., Hofmann, P.,Willhauck, G., Lingenfelder, I. and Heynen, M.(2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogrammetry Remote Sensing 58, Carlos M. Fonseca and Peter J. Fleming (1995), Evolutionary Computation, Springer. Bian, L. (1997). Multiscale Nature of Spatial Data in Scaling up Environmental Models, in Scale in Remote Sensing and GIS, Quattrochi, D.A. and Goodchild,M.F.(Eds), Lewis, pp Hay, J., Blashke, T.,Marceau, J. and Bouchard, A.(2003). A comparison of three imageobject methods for the multiscale analysis of landscape structure. ISPRS Journal of Photogrammetry & Remote Sensing, 57, pp Hay, G.J., Castilla, G., Wulder, M.A. and Ruiz, J.R.(2005). An automated object-based approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observation and Geoinformation, 7, pp Hammam, Y., Moore, A., and Whigham, P.(2007). "The dynamic geometry of Geographical Vector Agents",Computers, Environment and Urban Systems, vol.31, no.5, pp Gao, J. (2009). Digital Analysis of Remotely Sensed Imagery, McGrow-Hill, pp Goodchild, M.(2001). Issues in spatially explicit modelling. In D. Parker, T. Berger & S. M. Manson(Eds.), Agent-based models of land-use and landcover change (pp ). Irvine. Manson, S.M., Sun, S. and Bonsal, D.(2012). Agent-Based Models of Geographical Systems,Springer,pp Manson, S. M.(2007). Does scale exist? An epistemological scale continuum for complex human environment systems, Geoforum, Accepted in press. Moore, A.(2011). Geographical Vector Agent Based Simulation for Agricultural Land Use Modelling, in Advanced GeoSimulation Models, Marceau,D. and Benenson, I. (Eds), pp MacEachren, A. M., Robinson, A., Hopper, S., Gardner, S., Murray, R., Gahegan, M., and Hetzler E.(2005). Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know, Cartography and Geographic Information Science, Vol. 32, No. 3, 2005, pp Rouff, C.A., Hinchey M., Rash. J, Truszkowski, W., and Gordon,S. D. (Eds). (2006). Agent Technology from a Formal Perspective, Springer. Tian, J. and Chen, D.M. (2007). International Journal of Remote Sensing, ISSN print/issn , online Taylor & Francis. Torrens, P., and Benenson, I.(2003). "Geographic Automata Systems", International Journal of Geographic Information Science, vol. 10, no.4, pp Walsh, S. J., Moody, A., Allen, T. R., and Brown, D.G.(1997). Scale Dependence of NDVI and Its Relationship to Mountainous Terrain, in Scale in Remote Sensing and GIS, Quattrochi, D.A. and Goodchild,M.F.(Eds), Lewis, pp Yuan, M., Goodchild, M. F., Cova, T. J. (2007), Towards a General Theory of Geographic Representation in GIS, International Journal of Geographic Information Science, Volume 21, Pages