InforLorV4, Main, Exploration, bibRecord, 003135

A New Scheme for Land Cover Classification in Aerial Images: Combining Extended Dependency Tree-HMM and Unsupervised Segmentation

Identifieur interne : 003135 ( Main/Exploration ); précédent : 003134; suivant : 003136

A New Scheme for Land Cover Classification in Aerial Images: Combining Extended Dependency Tree-HMM and Unsupervised Segmentation

Auteurs : Mohamed El Yazid Boudaren [Algérie] ; Abdel Belaïd [France]

Source :

Lecture Notes in Electrical Engineering [ 1876-1100 ]

RBID : ISTEX:63B26940428718B8A2154583B445C600B7F58515

English descriptors

mix :
- Land cover classification, aerial images, hidden Markov model.

Abstract

Abstract: An important challenge to any image pixels classification system is to correctly assign each pixel to its proper class without blurring edges delimiting neighboring regions. In this paper, we present an aerial image mapping approach that advantageously combines unsupervised segmentation with a supervised Markov model based recognition. The originality of the proposed system carries on three concepts: the introduction of an auto-adaptive circular-like window size while applying our stochastic classification to preserve region edges, the extension of the Dependency Tree–HMM to permit the computation of likelihood probability on windows of different shapes and sizes and a mechanism that checks the coherence of the indexing by integrating both segmentations results: from unsupervised over segmentation, regions are assigned to the predominating class with a focus on inner region pixels. To validate our approach, we achieved experiments on real world high resolution aerial images. The obtained results outperform those obtained by supervised classification alone.

Url:

DOI: 10.1007/978-90-481-8776-8_40

Affiliations:

Le document en format XML

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A New Scheme for Land Cover Classification in Aerial Images: Combining Extended Dependency Tree-HMM and Unsupervised Segmentation

A New Scheme for Land Cover Classification in Aerial Images: Combining Extended Dependency Tree-HMM and Unsupervised Segmentation

Source :

English descriptors

Abstract

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