Robust aerial image mosaicing algorithm based on fuzzy outliers rejection
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ORIGINAL PAPER
Robust aerial image mosaicing algorithm based on fuzzy outliers rejection Abdelhai Lati1,2 · Mahmoud Belhocine3 · Noura Achour1,2 Received: 19 November 2018 / Accepted: 4 March 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract The use of unmanned aerial vehicles (UAVs) imagery for acquiring data is constantly evolving, due to the ease of use and low data acquisition costs. All of these made UAVs very popular with end customers and data acquisition companies. For most cases, the acquired UAV images need further more processing before being analyzed, and that because of changing of illumination condition in aerial environment and fog generated because of UAV flying speed. Image mosaicing is a practical solution for these problems; in which overlapped views of the same scene are combined to form a large image with high quality. The common problem associated with image mosaicing algorithms is the false associations (outliers) produced when defining the overlapping region between every two successive views. This article presents an image mosaicing algorithm based on efficient fuzzy technique for outliers rejection. Our proposed technique is based on using RANdom SAmpling Consensus (RANSAC) and bidirectional approaches with a fuzzy inference system in order to separate between inliers and outliers. The experimental results prove that the proposed method has good performance for aerial images and gives better results when compared with other techniques. Thus our approach is insensitive to the ordering, orientation, scale and illumination of the images. Keywords UAV images · Bidirectional condition · RANSAC · Fuzzy outliers rejection
1 Introduction Nowadays, because of their excellent maneuverability, the unmanned aerial vehicles (UAVs) are widely used in different applications such as exploration, aerial photography, agricultural, disaster observation and military surveillance. Current generation of UAVs can be transported in small * Abdelhai Lati [email protected] Mahmoud Belhocine [email protected] Noura Achour [email protected] 1
Université de Sciences et Technologie de Houari Boumedian USTHB, Alger, Algeria
2
Laboratoire de Robotique, Parallélisme et Systèmes Embarqués LRPSE, BP 32, El Alia, Bab Ezzouar, Alger 16111, Algeria
3
Centre du Développement des Technologies Avancées CDTA, Cité 20 août 1956, Baba Hassen, Alger 16303, Algeria
vehicles and launched from a road or a small truck but are still large enough to be equipped with cameras and sensors that can provide low cost aerial information (John et al. 2013), vision sensors may estimate state of UAVs, therefore, they give an alternative navigation system for aerial drones when global positioning system (GPS) doesn’t work, or when the global coordinates of GPS cannot be available because of number of factors, such as interference, weather, and mission objectives, in that case; visual self-localization based on fuzzy rules could solve the problem (Zhou 2007). For position estimation, it is often necessary to an
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