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Multi-resolution Classification of Urban Areas Using Hierarchical Symmetric Markov Mesh Models
Abstract : In this paper we investigate a new hierarchical method for high resolution remotely sensed image classification. The proposed approach integrates an explicit hierarchical graph-based classifier, which uses a quad-tree structure to model multi-scale interactions, and a symmetric Markov mesh random field to deal with pixelwise contextual information at the same scale. The choice of a quad-tree and the symmetric Markov mesh allow taking benefit from their good analytical properties (especially causality) and consequently applying time-efficient non-iterative inference algorithms.
Cited literature [16 references]
https://hal.inria.fr/hal-01415568
Contributor : Ihsen Hedhli <>
Submitted on : Tuesday, December 13, 2016 - 11:57:05 AM
Last modification on : Tuesday, September 8, 2020 - 2:48:17 PM
Long-term archiving on: : Tuesday, March 14, 2017 - 12:49:47 PM
Contributor : Ihsen Hedhli <>
Submitted on : Tuesday, December 13, 2016 - 11:57:05 AM
Last modification on : Tuesday, September 8, 2020 - 2:48:17 PM
Long-term archiving on: : Tuesday, March 14, 2017 - 12:49:47 PM
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