Image Mining Using Directional Spatial Constraints

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Date

2010-01-01

Author

AKSOY, SELİM
Cinbiş, Ramazan Gökberk

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Spatial information plays a fundamental role in building high-level content models for supporting analysts' interpretations and automating geospatial intelligence. We describe a framework for modeling directional spatial relationships among objects and using this information for contextual classification and retrieval. The proposed model first identifies image areas that have a high degree of satisfaction of a spatial relation with respect to several reference objects. Then, this information is incorporated into the Bayesian decision rule as spatial priors for contextual classification. The model also supports dynamic queries by using directional relationships as spatial constraints to enable object detection based on the properties of individual objects as well as their spatial relationships to other objects. Comparative experiments using high-resolution satellite imagery illustrate the flexibility and effectiveness of the proposed framework in image mining with significant improvements in both classification and retrieval performance.

Subject Keywords

Geotechnical Engineering and Engineering Geology, Electrical and Electronic Engineering

URI

https://hdl.handle.net/11511/38429

Journal

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

DOI

https://doi.org/10.1109/lgrs.2009.2014083

Collections

Department of Computer Engineering, Article

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Citation Formats

S. AKSOY and R. G. Cinbiş, “Image Mining Using Directional Spatial Constraints,” IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, pp. 33–37, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38429.