Method for determining elements of urban infrastructure objects based on the results from air monitoring
DOI:
https://doi.org/10.15587/1729-4061.2019.174576Keywords:
air monitoring system, element of an urban infrastructure object, Canny method, Hough transformAbstract
The study proposes a two-stage method for determination of elements of urban infrastructure objects in images made by air monitoring systems. The first stage implies determining the contours of objects in images. The advanced Canny method was selected as the contour determination method. We considered the main stages of the advanced Canny method for determination of contours of objects in images made by air monitoring systems. The application of the Hough transform at the second stage was proposed.
The paper reports features in the method for determination of elements of urban infrastructure in color images made by air monitoring systems. In contrast to known methods, the method takes into account features of formation of images made by air monitoring systems. It highlights color channels and marks out contours and geometric primitives in each color channel; it re-integrated color channels and determines elements of urban infrastructure objects in the space of an output image.
The study presents the results of applying the method for determination of elements of urban infrastructure objects in a standard color image acquired from an air monitoring system. We defined elements of urban infrastructure objects, such as roads, houses, streets, building elements and others, as an example.
A visual evaluation of the quality of processing of a typical color image made by an air monitoring system was performed. We calculated errors of the first kind and the second kind. It was established that application of a two-stage method for determination of elements of urban infrastructure objects in an image made by an air monitoring system improves the quality of processing of optoelectronic images. Moreover, errors of the first kind and the second kind in determination of elements of urban infrastructure objects reduced by 13 % on average.References
- Chemin, Y. (Ed.) (2012). Remote Sensing of Planet Earth. Rijeka. doi: https://doi.org/10.5772/2291
- Richards, J. (2013). Remote Sensing Digital Image Analysis. An Introduction. Springer. doi: https://doi.org/10.1007/978-3-642-30062-2
- Vysotska, V., Lytvyn, V., Burov, Y., Gozhyj, A., Makara, S. (2018). The consolidated information web-resource about pharmacy networks in city. CEUR Workshop Proceedings (Computational linguistics and intelligent systems), 2255, 239–255.
- Lytvyn, V., Vysotska, V. (2015). Designing architecture of electronic content commerce system. 2015 Xth International Scientific and Technical Conference “Computer Sciences and Information Technologies” (CSIT). doi: https://doi.org/10.1109/stc-csit.2015.7325446
- Stryzhak, O., Prychodniuk, V., Podlipaiev, V. (2019). Model of Transdisciplinary Representation of GEOspatial Information. Advances in Information and Communication Technologies, 34–75. doi: https://doi.org/10.1007/978-3-030-16770-7_3
- Yang, X. (Ed.) (2011). Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment. John Wiley & Sons. doi: https://doi.org/10.1002/9780470979563
- Saito, S., Aoki, Y. (2015). Building and road detection from large aerial imagery. Image Processing: Machine Vision Applications VIII. doi: https://doi.org/10.1117/12.2083273
- Dempsey, N., Brown, C., Raman, S., Porta, S., Jenks, M., Jones, C., Bramley, G. (2008). Elements of Urban Form. Sustainable City Form, 21–51. doi: https://doi.org/10.1007/978-1-4020-8647-2_2
- Gonzalez R. C., Woods R. E. (2017). Digital Image Processing. Prentice Hall, 1192.
- Gupta, V., Singh, D., Sharma, P. (2016). Image Segmentation Using Various Edge Detection Operators: A Comparative Study. International Journal of Innovative Research in Computer and Communication Engineering, 4 (8), 14819–14824.
- Sum, K., S. Cheung, P. (2006). A Fast Parametric Snake Model with Enhanced Concave Object Extraction Capability. 2006 IEEE International Symposium on Signal Processing and Information Technology. doi: https://doi.org/10.1109/isspit.2006.270844
- Liu, Y., Yao, J., Lu, X., Xia, M., Wang, X., Liu, Y. (2019). RoadNet: Learning to Comprehensively Analyze Road Networks in Complex Urban Scenes From High-Resolution Remotely Sensed Images. IEEE Transactions on Geoscience and Remote Sensing, 57 (4), 2043–2056. doi: https://doi.org/10.1109/tgrs.2018.2870871
- Nyandwi, E., Koeva, M., Kohli, D., Bennett, R. (2019). Comparing Human Versus Machine-Driven Cadastral Boundary Feature Extraction. Remote Sens, 11, 1662. doi: https://doi.org/10.20944/preprints201905.0342.v1
- Ruban, I., Khudov, H., Khudov, V., Khizhnyak, I., Makoveichuk, O. (2017). Segmentation of the images obtained from onboard optoelectronic surveillance systems by the evolutionary method. Eastern-European Journal of Enterprise Technologies, 5 (9 (89)), 49–57. doi: https://doi.org/10.15587/1729-4061.2017.109904
- Ruban, I., Khudov, V., Khudov, H., Khizhnyak, I. (2017). An improved method for segmentation of a multiscale sequence of optoelectronic images. 2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). doi: https://doi.org/10.1109/infocommst.2017.8246367
- Ruban, I., Khudov, V., Makoveichuk, O., Khudov, H., Khizhnyak, I. (2018). A Swarm Method for Segmentation of Images Obtained from On-Board Optoelectronic Surveillance Systems. 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). doi: https://doi.org/10.1109/infocommst.2018.8632045
- Ruban, I., Khudov, H., Makoveichuk, O., Khizhnyak, I., Khudov, V., Podlipaiev, V. et. al. (2019). Segmentation of optical-electronic images from on-board systems of remote sensing of the earth by the artificial bee colony method. Eastern-European Journal of Enterprise Technologies, 2 (9 (98)), 37–45. doi: https://doi.org/10.15587/1729-4061.2019.161860
- Ramlau, R., Scherzer, O. (Eds.) (2019). The Radon Transform. Berlin/Boston: Walter de Gruyter GmbH. doi: https://doi.org/10.1515/9783110560855
- Li, Z., Liu, Y., Walker, R., Hayward, R., Zhang, J. (2009). Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform. Machine Vision and Applications, 21 (5), 677–686. doi: https://doi.org/10.1007/s00138-009-0206-y
- Kabade, A., Sangam, V. (2016). Canny edge detection algorithm. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), 5 (5), 1292–1295.
- Manzanera, A., Nguyen, T. P., Xu, X. (2016). Line and circle detection using dense one-to-one Hough transforms on greyscale images. EURASIP Journal on Image and Video Processing, 2016 (1). doi: https://doi.org/10.1186/s13640-016-0149-y
- El-Baz, A., Jiang, X., Jasjit, S. (Eds.) (2016). Biomedical image segmentation: advances and trends. CRC Press, 546. doi: https://doi.org/10.4324/9781315372273
- Choudhary, R., Gupta, R. (2017). Recent Trends and Techniques in Image Enhancement using Differential Evolution- A Survey. International Journal of Advanced Research in Computer Science and Software Engineering, 7 (4), 106–112. doi: https://doi.org/10.23956/ijarcsse/v7i4/0108
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Nataliia Lukova-Chuiko, Hennady Pevtsov, Yurii Sheviakov, Iryna Yuzova, Yevhen Drob, Olexander Tytarenko
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.