JOINT ROAD NETWORK EXTRACTION FROM A SET OF HIGH RESOLUTION SATELLITE IMAGES

被引：0

作者：

Besbes, O. ^{[1
]}

Benazza-Benyahia, A. ^{[2
]}

机构：

[1] Sousse Univ, ISITCOM, COSIM Lab, GP 1, Hammam Sousse 4011, Tunisia

[2] Carthage Univ, SUPCOM, COSIM Lab, Cite Technol, Ariana 2080, Tunisia

来源：

2014 PROCEEDINGS OF THE 22ND EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2014年

关键词：

Road network; joint segmentation; CRF; RECOGNITION; MULTICLASS;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we develop a novel Conditional Random Field (CRF) formulation to jointly extract road networks from a set of high resolution satellite images. Our fully unsupervised method relies on a pairwise CRF model defined over a set of test images, which encodes prior assumptions about the roads such as thinness, elongation. Four competitive energy terms related to color, shape, symmetry and contrast-sensitive potentials are suitably defined to tackle with the challenging problem of road network extraction. The resulting objective energy is minimized by resorting to graph-cuts tools. Promising results are obtained for developed suburban scenes in remotely sensed images. The proposed model improve significantly the segmentation quality, compared against the independent CRF and two state-of-the-art methods.

引用

页码：2190 / 2194

页数：5

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