Large-scale Satellite Image Browsing using Automatic Semantic Categorization
and Content-based Retrieval

Ashish Parulekar, Ritendra Datta, Jia Li, James Z. Wang
The Pennsylvania State University

We approach the problem of large-scale satellite image browsing from a content-based retrieval and semantic categorization perspective. A two-stage method for query based automatic retrieval of satellite image patches is proposed. The semantic category of query patches are determined and patches from that category are ranked based on an image similarity measure. Semantic categorization is done by a learning approach involving the two-dimensional multi-resolution hidden Markov model (2-D MHMM). Patches that do not belong to any trained category are handled using a support vector machine (SVM) based classifier. Experiments yield promising results in modeling semantic categories within satellite images using 2-D MHMM, producing accurate and convenient browsing. We also show that prior semantic categorization improves retrieval performance.

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Citation: Ashish Parulekar, Ritendra Datta, Jia Li and James Z. Wang, ``Large-scale Satellite Image Browsing using Automatic Semantic Categorization and Content-based Retrieval,'' Proceedings of the IEEE International Workshop on Semantic Knowledge in Computer Vision, in conjunction with IEEE International Conference on Computer Vision, 8 pages, Beijing, China, IEEE, October 2005.

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Last Modified: Mon Aug 29 18:38:04 EDT 2005
2005, James Z. Wang