Quantification of GNSS signals accuracy: An image segmentation method for estimating the percentage of sky

This paper is focused on the characterisation of the GNSS reception signals environment by estimating the percentage of visible sky. The estimation is based on a new segmentation technique that uses color and texture gradients with an adaptive and non-parametric combination strategy. The structural gradient, resulting from the combination, is processed with the watershed algorithm to get image segmentation. The classification process used to extract the sky region is performed using the k-means algorithm. Experimental segmentation and classification results, using real data and an evaluation methodology, are presented to demonstrate the effectiveness and the reliability of the proposed approach.

[1]  Jun-ichi Meguro,et al.  GPS Multipath Mitigation for Urban Area Using Omnidirectional Infrared Camera , 2009, IEEE Transactions on Intelligent Transportation Systems.

[2]  E. Duflos,et al.  Gnss performance enhancement in urban environment based on pseudo-range error model , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[3]  Yang Gao,et al.  High-Sensitivity GPS Data Classification Based on Signal Degradation Conditions , 2007, IEEE Transactions on Vehicular Technology.

[4]  Jesús Angulo,et al.  Color segmentation by ordered mergings , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[5]  Jun-ichi Meguro,et al.  GPS accuracy improvement by satellite selection using omnidirectional infrared camera , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Abderrahim Elmoataz,et al.  Graph-Based Ordering Scheme for Color Image Filtering , 2008, Int. J. Image Graph..

[8]  Patrick Robertson,et al.  Bayesian Time Delay Estimation of GNSS Signals in Dynamic Multipath Environments , 2008 .

[9]  Serge Beucher,et al.  Watershed, Hierarchical Segmentation and Waterfall Algorithm , 1994, ISMM.

[10]  Josef Kittler,et al.  Automatic watershed segmentation of randomly textured color images , 1997, IEEE Trans. Image Process..

[11]  Juliette Marais,et al.  Land mobile GNSS availability and multipath evaluation tool , 2005, IEEE Transactions on Vehicular Technology.

[12]  O. Lezoray,et al.  A graph approach to color mathematical morphology , 2005, Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005..

[13]  J. Angulo Gradients morphologiques de texture. Application à la segmentation couleur+texture par LPE , 2006 .

[14]  V. Barnett The Ordering of Multivariate Data , 1976 .