Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Self-organizing maps
Automatic Extraction of Man-Made Objects from Aerial & Space Images
Automatic Extraction of Man-Made Objects from Aerial & Space Images
A new and efficient k-medoid algorithm for spatial clustering
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Iterative and localized radon transform for road centerline detection from classified imagery
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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An approach to semi-automated linear feature extraction from aerial imagery is introduced in which Kohonen's self-organizing map (SOM) algorithm is integrated with existing GIS data. The SOM belongs to a distinct class of neural networks which is characterized by competitive and unsupervised learning. Using radiometrically classified image pixels as input, appropriate SOM network topologies are modeled to extract underlying spatial structures contained in the input patterns. Coarse-resolution GIS vector data is used for network weight and topology initialization when extracting specific feature components. The Kohonen learning rule updates the synaptic weight vectors of winning neural units that represent 2-D vector shape vertices. Experiments with high-resolution hyperspectral imagery demonstrate a robust ability to extract centerline information when presented with coarse input.