Hierarchical MRF-based segmentation of remote-sensing images

被引：0

作者：

Gaetano, R. ^{[1
]}

Poggi, G. ^{[1
]}

Scarpa, G. ^{[1
]}

机构：

[1] Univ Naples Federico II, Dipartimento Ingn Elettron & Telecommun, Via Claudio, 21, I-80125 Naples, Italy

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS | 2006年

关键词：

hierarchical image segmentation; tree structured; markov random field; mean shift; remote sensing images;

D O I：

10.1109/ICIP.2006.312753

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Remote-sensing images are often composed by a hierarchy of nested regions, with complex regions that are regarded as homogeneous at some observation scale, but can be further segmented at finer scales. Tree-structured Markov random fields (TS-MRF) allow one to model such images, and to develop efficient segmentation algorithms for them. TS-MRF are traditionally based on binary trees of classes, but the use of generic trees, with more degrees of freedom, can likely provide a better performance, as was shown in [1] with reference to synthetic images. Here we build upon the ideas proposed in [1] to devise a segmentation algorithm that works effectively, and with a limited computational burden, on real-world remote sensing images.

引用

页码：1121 / +

页数：2

共 6 条

[1] Mean shift: A robust approach toward feature space analysis [J].