Détection simultanée de l’ombre et de la végétation sur des images aériennes couleur en haute résolution

Détection simultanée de l’ombre et de la végétation sur des images aériennes couleur en haute résolution

Tran-Thanh Ngo Christophe Collet  Vincent Mazet 

ICube, Université de Strasbourg - CNRS 300 Bd Sébastien Brant, CS 10413, F-67412 Illkirch cedex, France

Corresponding Author Email: 
{ttngo,c.collet,vincent.mazet}@unistra.fr
Page: 
311-333
|
DOI: 
https://doi.org/10.3166/TS.32.311-333
Received: 
12 December 2014
| |
Accepted: 
20 July 2015
| | Citation

OPEN ACCESS

Abstract: 

We propose a new method to simultaneously detect shadows and vegetation in remote sensing images. Several shadow and vegetation indices are merged with the Dempster-Shafer (DS) theory so as to increase the reliability and accuracy of the segmentation. The per-pixel DS fusion is incorporated in a Markovian context to obtain an optimal and regularized segmentation result. Evaluation on aerial images clearly shows that this new method is robust and accurate.

RÉSUMÉ

Nous proposons une nouvelle méthode pour détecter simultanément les zones d’ombre et de végétation dans des images de télédétection. Plusieurs indices d’ombre et de végétation sont fusionnés grâce à la théorie de DS afin d’augmenter la fiabilité et la précision de la segmentation. La fusion de DS étant une méthode pixellique, elle est incorporée dans un contexte markovien pour obtenir une segmentation régularisée et plus optimale. Les évaluations sur des images aériennes montrent clairement que cette méthode est robuste et précise.

Keywords: 

remote sensing, Dempster-Shafer, Markov random field, shadow detection, vegetation detection, multivariate segmentation

MOTS-CLÉS

télédétection, Dempster-Shafer, champs de Markov cachés, détection d’ombre, détection de végétation, segmentation multivariée

1. Introduction
2. Modèles Photométriques Invariants À La Couleur
3. Approche Proposée
4. Résultats Obtenus
5. Discussion
6. Conclusions
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