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
1.1. Image Classification What is the image classification?
1.2. Methods A. Pixel-based: based on spectral reflectance Supervised Classification Unsupervised Classification
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
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.
1.2. Methods B C compactness cpt l n Based on slide by Jarlath O Neil Dunne by Austin Troy and Weiqi Zhou, 2008
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
1.3. Limitations may we have a correct extraction and shaping of interest objects
1.3.1. Proposed Method They are objects which can support a dynamic and irregular geometry: Geographical Vector Agents (VA) Moore et al. (2011)
3. 1. Image Object Geometry in The Context of The Vector Agent
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
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 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
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
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