ORIENTED TRIPLET MARKOV FIELDS FOR HYPERSPECTRAL IMAGE SEGMENTATION

被引：0

作者：

Courbot, Jean-Baptiste ^{[1
,3
]}

Monfrini, Emmanuel ^{[2
]}

Mazet, Vincent ^{[1
]}

Collet, Christophe ^{[1
]}

机构：

[1] Univ Strasbourg, CNRS, ICube, F-67412 Illkirch Graffenstaden, France

[2] CNRS, Dept CITI, SAMOVAR, F-91011 Evry, France

[3] Univ Lyon 1, Univ Lyon, Ens Lyon, CNRS,CRAL UMR5574, F-69230 St Genis Laval, France

来源：

2016 8TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS) | 2016年

关键词：

Triplet Markov Field; Bayesian Segmentation; Orientation Retrieving;

D O I：

暂无

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

摘要：

Hyperspectral image processing benefits greatly from using spatial information. Markov field modeling is a well-known statistical model class for considering spatial relationships between sites of an image. Often, the model restricts to Hidden Markov Field, therefore cannot handle non-stationarities in the images. This paper presents a Triplet Markov Field model for hyperspectral image segmentation, allowing the joint retrieving of image classes and local orientations. Segmentation results on synthetic data validate the methods, and results on real astronomical data are presented.

引用

页数：5

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