Detection of compound structures using hierarchical clustering of statistical and structural features

Date
2011
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Source Title
2011 IEEE International Geoscience and Remote Sensing Symposium
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Electronic ISSN
Publisher
IEEE
Volume
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Pages
2385 - 2388
Language
English
Type
Conference Paper
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Abstract

We describe a new procedure that combines statistical and structural characteristics of simple primitive objects to discover compound structures in images. The statistical information that is modeled using spectral, shape, and position data of individual objects, and structural information that is modeled in terms of spatial alignments of neighboring object groups are encoded in a graph structure that contains the primitive objects at its vertices, and the edges connect the potentially related objects. Experiments using WorldView-2 data show that hierarchical clustering of these vertices can find high-level compound structures that cannot be obtained using traditional techniques. © 2011 IEEE.

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Keywords
alignment detection, graph-based representation, hierarchical clustering, Object detection, Compound structures, Graph structures, Graph-based representations, Hier-archical clustering, Individual objects, Object Detection, Object groups, Position data, Spatial alignment, Statistical information, Structural characteristics, Structural feature, Structural information, Traditional techniques, Alignment, Remote sensing, Geology
Citation
Published Version (Please cite this version)