Authors:
Y. El Merabet
1
;
Y. Ruichek
2
;
S. Ghaffarian
3
;
Z. Samir
1
;
T. Boujiha
1
;
R. Touahni
1
;
R. Messoussi
1
and
A. Sbihi
4
Affiliations:
1
Université Ibn Tofail, Morocco
;
2
Université de Technologie de Belfort-Montbéliard, France
;
3
University of Twente, Netherlands
;
4
Université Abdelmalek Essadi, Morocco
Keyword(s):
GNSS, Region Classification, Image Segmentation, Fisheye, Color Invariance, Hellinger Kernel, Local Image Region Descriptors.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Entertainment Imaging Applications
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
Characterizing GNSS signals reception environment using fisheye camera oriented to the sky is one of the relevant approaches which have been proposed to compensate the lack of performance of GNSS occurring when operating in constrained environments (dense urbain areas). This solution consists, after classification of acquired images into two regions (sky and not-sky), in identifying satellites as line-of-sight (LOS) satellites or non-line-of-sight (NLOS) satellites by repositioning the satellites in the classified images. This paper proposes a region-based image classification method through local image region descriptors and Hellinger kernel-based distance. The objective is to try to improve results obtained previously by a state of the art method. The proposed approach starts by simplifying the acquired image with a suitable couple of colorimetric invariant and exponential transform. After that, a segmentation step is performed in order to extract from the simplified image regions
of interest using Statistical Region Merging method. The next step consists of characterizing the obtained regions with local RGB color and a number of local color texture descriptors using image quantization. Finally, the characterized regions are classified into sky and non sky regions by using supervised MSRC (Maximal Similarity Based Region Classification) method through Hellinger kernel-based distance. Extensive experiments have been performed to prove the effectiveness of the proposed approach.
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