Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadata

Cited literature [16 references]  Display  Hide  Download

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

File

Hedhli_Jurse_Final.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01415568, version 1

Collections

Citation

Ihsen Hedhli, Gabriele Moser, Sebastiano Serpico, Josiane Zerubia. Multi-resolution Classification of Urban Areas Using Hierarchical Symmetric Markov Mesh Models. IEEE GRS/ISPRS Joint Urban Remote Sensing Event (JURSE), Mar 2017, Dubai, United Arab Emirates. ⟨hal-01415568⟩

Share

Metrics

Record views

379

Files downloads

328