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Open AccessArticle

Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network

by Shenggu Yuan 1, Ke Yang 2,*, Xin Li 3 and Hongyue Cai 3
1
China Transport Telecommunications and Information Center, Beijing 100011, China
2
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
3
Guojiao Spatial Information Technology (Beijing) Co., Ltd., Beijing 100011, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 1832; https://doi.org/10.3390/s20071832
Received: 10 February 2020 / Revised: 12 March 2020 / Accepted: 24 March 2020 / Published: 26 March 2020
Image mosaicking which is a process of constructing multiple orthoimages into a single seamless composite orthoimage, is one of the key steps for the production of large-scale digital orthophoto maps (DOM). Seamline determination is one of the most difficult technologies in the automatic mosaicking of orthoimages. The seamlines that follow the centerlines of roads where no significant differences exist are beneficial to improve the quality of image mosaicking. Based on this idea, this paper proposes a novel method of seamline determination based on road probability map from the D-LinkNet neural network for urban image mosaicking. This method optimizes the seamlines at both the semantic and pixel level as follows. First, the road probability map is obtained with the D-LinkNet neural network and related post processing. Second, the preferred road areas (PRAs) are determined by binarizing the road probability map of the overlapping area in the left and right image. The PRAs are the priority areas in which the seamlines cross. Finally, the final seamlines are determined by Dijkstra’s shortest path algorithm implemented with binary min-heap at the pixel level. The experimental results of three group data sets show the advantages of the proposed method. Compared with two previous methods, the seamlines obtained by the proposed method pass through the less obvious objects and mainly follow the roads. In terms of the computational efficiency, the proposed method also has a high efficiency. View Full-Text
Keywords: mosaicking; urban image; seamline determination; deep learning; D-LinkNet mosaicking; urban image; seamline determination; deep learning; D-LinkNet
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Yuan, S.; Yang, K.; Li, X.; Cai, H. Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network. Sensors 2020, 20, 1832.

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