Abstract
The coastline can be used as the demarcation line between the ocean and the continent, which plays a crucial role in the development of marine economy. The purpose of this paper is to achieve stitching of coastline images from different areas automatically and create panoramic coast images. To be specific, we proposed a multi-resolution image fusion method which is based on the optimal seam-line method to solve the huge shadow and unnatural image connection problems which caused by the process of the fused region of coastline splicing. In this paper, we adopted the multi-resolution wavelet fusion method by using multi-resolution wavelet fusion method in searching the optimal seam-line. At the same time, it is found in the contrast experiment that the image fused by the proposed algorithm has no obvious shadow situation and can be better fused in vision.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bellman RE, Dreyfus SE, Bellman R et al (2015) Applied dynamic programming: on a transcendental curve. Princeton University Press https://scholar.google.com.hk/scholar?cluster=15518837431529322383&hl=zh-CN&as_sdt=0,5
Duplaquet ML (1998) Building large image mosaics with invisible seam lines. In: Visual information processing VII, vol 3387. International Society for Optics and Photonics, pp 369–378. https://doi.org/10.1117/12.316427
Fakhari F, Mosavi MR, Lajvardi MM (2017) Image fusion based on multi-scale transform and sparse representation: image energy approach. IET Image Process 11(11):1041–1049. https://doi.org/10.1049/iet-ipr.2017.0104
Hejazifar H, Khotanlou H (2018) Fast and robust seam estimation to seamless image stitching. SIViP:1–9. https://doi.org/10.1007/s11760-017-1231-3
Kwatra V, Schödl A, Essa I et al (2003) Graphcut textures: image and video synthesis using graph cuts. ACM Trans Graph 22(3):277–286. https://doi.org/10.1145/1201775.882264
Li S, Kang X, Fang L et al (2017) Pixel-level image fusion. Inf Fusion 33(C):100–112. https://doi.org/10.1016/j.inffus.2016.05.004
Lin CC, Pankanti SU, Ramamurthy KN et al (2015) Adaptive as-natural-as-possible image stitching. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1155–1163. https://doi.org/10.1109/CVPR.2015.7298719
Liu T, Zhang J (2018) Improved algorithm for remote sensing image mosaic of UAV based on ORB feature. Comput Eng Appl 2:031
Liu L, Xu W, Yue Q et al (2018) Problems and countermeasures of coastline protection and utilization in China. Ocean Coast Manag 153:124–130. https://doi.org/10.1016/j.ocecoaman.2017.12.016
Ma J, Zhou H, Zhao J et al (2015) Robust feature matching for remote sensing image registration via locally linear transforming. IEEE Trans Geosci Remote Sens 53(12):6469–6481. https://doi.org/10.1109/TGRS.2015.2441954
Rani K, Sharma R (2013) Study of different image fusion algorithm. Int J Emerg Technol Adv Eng 3(5):288–291 https://scholar.google.com.hk/scholar?cluster=6489454236133578593&hl=zh-CN&as_sdt=0,5
Samuel M, Mohamad M, Saad SM et al (2018) Development of edge-based lane detection algorithm using image processing. Int J Inform Vis 2(1):19–22. https://doi.org/10.30630/joiv.2.1.101
Vedaldi A, Fulkerson B (2010) VLFeat: an open and portable library of computer vision algorithms. In: Proceedings of the 18th ACM international conference on multimedia. ACM, pp 1469–1472. https://doi.org/10.1145/1873951.1874249
Xiong J, Zhang Y, Lin L, Shen J, Li X, Lin M (2017) ms-PoSW: A multi-server aided proof of shared ownership scheme for secure deduplication in cloud. Concurr Comput Pract Exp. https://doi.org/10.1002/cpe.4252
Xiong J, Ren J, Chen L, Yao Z, Lin M, Wu D, Niu B (2019) Enhancing privacy and availability for data clustering in intelligent electrical service of IoT. IEEE Internet Things J 6(2):1530–1540. https://doi.org/10.1109/JIOT.2018.2842773
Yu G, Yang Z, Ren G et al (2017) Image stitching combined with optimal stitching and multi-resolution fusion. J Image Graphics 22(6):842–851
Zeng L, Zhang S, Zhang J et al (2014) Dynamic image mosaic via SIFT and dynamic programming. Mach Vis Appl 25(5):1271–1282. https://doi.org/10.1007/s00138-013-0551-8
Zhang Z, Matsushita Y, Ma Y (2011) Camera calibration with lens distortion from low-rank textures. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2321–2328. https://doi.org/10.1109/CVPR.2011.5995548
Zhang X, Zhang X, Yang B et al (2013) Remote sensing extraction of coastline based on coastal type and tidal level correction. Remote Sens Land Resour 25(4):91–97. https://doi.org/10.6046/gtzyyg.2013.04.15
Zou J, Sun W (2018) Multi-focus image fusion based on lifting static wavelet transform and sparse representation of joint structure group. J Comput Appl 38(3):859–865
Acknowledgements
This work is supported by the Natural Science Foundation of China (Grant No. 61702316), the Natural Science Foundation of Shanxi Provincial, China (Grant No. 201801D221177).
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by: H. Babaie
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, B., Li, H. & Hu, W. Research on key techniques of multi-resolution coastline image fusion based on optimal seam-line. Earth Sci Inform 13, 333–344 (2020). https://doi.org/10.1007/s12145-019-00421-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-019-00421-z