Algorithm for analysis of multispectral aerial images from uav for identification of water pollution using analytical methods and neural network approaches
- 作者: Diane S.A.1, Vytovtov K.A.1, Barabanova E.A.1
-
隶属关系:
- V.A. Trapeznikov Institute of Control Sciences of RAS
- 期: 编号 108 (2024)
- 页面: 98-123
- 栏目: Information technologies in control
- URL: https://bakhtiniada.ru/1819-2440/article/view/284356
- DOI: https://doi.org/10.25728/ubs.2024.108.6
- ID: 284356
如何引用文章
全文:
详细
The article is devoted to the development of algorithms for the analysis of pollution on the surface of water bodies based on visual information obtained using a multispectral camera mounted on the body of a UAV. The structure of the algorithmic complex for the analysis of multispectral aerial photographs is proposed. Within the framework of the developed approach, each of the analyzed images undergoes a preprocessing procedure that ensures the alignment and alignment of its spectral channels into a single multidimensional raster. The developed analytical algorithm makes it possible to process and convolve the channels of a multispectral image using three mathematical operators - bandpass filtering, contrast change, and brightness change. At the same time, the choice of parameters for identifying pollution on the surface of water bodies is based on a preliminary stage associated with maximizing the contrast excess index for the reference area. The proposed neural network pollution analysis algorithm is based on the application of the sliding window method in combination with the convolutional architecture of the neural network classifier for the analysis of image fragments located on a rectangular grid. The software implementation of these algorithms, as well as the development of a graphical user interface, made it possible to confirm the assumption about the effectiveness of each of the considered approaches. Experimental studies have shown that the neural network algorithm wins in accuracy, and the analytical approach is easier to interpret from the point of view of an expert.
作者简介
Sekou Diane
V.A. Trapeznikov Institute of Control Sciences of RAS
Email: diane1990@yandex.ru
Moscow
Konstantin Vytovtov
V.A. Trapeznikov Institute of Control Sciences of RAS
Email: vytovtov_konstan@mail.ru
Moscow
Elizaveta Barabanova
V.A. Trapeznikov Institute of Control Sciences of RAS
Email: elizavetaalexb@yandex.ru
Moscow
参考
- ХАЙКИН С. Нейронные сети: полный курс. – М.: Виль-ямс, 2008. 1103 с.
- ALI N.M., KARIS M.S., SOBRAN N.M.M., BAHAR M.B. et al. Detection of multiple mangoes using histogram of ori-ented gradient technique in aerial monitoring // ARPN Journal of Engineering and Applied Sciences. 2017. Vol. 12 (8). P. 2730–2736.
- BARRIENTOS Z., NÁJERA J.M., ZÚÑIGA M. A Satellite and Ground Evaluation of Urban Vegetation and Infrastruc-ture in the Landscape of a Tropical City: Heredia, Costa Ri-ca // Cities and the environment. – 2013.
- CONG Z., YAN G., SHENGHUI F., KAILI Y. et al. Com-bining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index // Frontiers in Plant Science. 2022. Vol. 13. – doi: 10.3389/fpls.2022.957870.
- KANTH P.C., GUPTA N., ARI S. Change Detection in Mul-tispectral Satellite Images using Histogram based Thresh-olding Technique // Proc. of 2019 IEEE Int. Conf. on Elec-trical, Computer and Communication Technologies (ICECCT), Coimbatore, India. 2019. P. 1–5. – doi: 10.1109/ICECCT.2019.8869500.
- MENG L., ZHOU J., LIU S., DING L. et al. Investigation and evaluation of algorithms for unmanned aerial vehicle multispectral image registration, International // Journal of Applied Earth Observation and Geoinformation. 2021. – Vol. 102, 102403. – doi: 10.1016/j.jag.2021.102403.
- MOGHIMI A., YANG C., ANDERSON J.A. Aerial hyper-spectral imagery and deep neural networks for high-throughput yield phenotyping in wheat // Computers and Electronics in Agriculture. 2020. Vol. 172. – doi: 10.1016/j.compag.2020.105299.
- PYKA K. Wavelet-Based Local Contrast Enhancement for Satellite, Aerial and Close Range Images // Remote Sens. – 2017. – Vol. 9. – P. 25. – doi: 10.3390/rs9010025.
- RODRIGUES R.B., PELLEGRINO S., PISTORI H. Combin-ing Color and Haar Wavelet Responses for Aerial Image Classification // In: Artificial Intelligence and Soft Compu-ting / Eds. – Springer Berlin Heidelberg, 2012. Vol. 7267. P. 583–591. – doi: 10.1007/978-3-642-29347-4_68.
- RUBLEE E., RABAUD V., KONOLIGE K., BRADSKI G. ORB: an efficient alternative to SIFT or SURF // Proc. of the IEEE Int. Conf. on Computer Vision. 2011. P. 2564–2571.
- VASILYEV A.V., MELNIKOVA I.N., POBEROV-SKAYA L.N., TOVSTENKO I.A. Spectral brightness coeffi-cient of natural ground surfaces in spectral ranges 0.35 to 0.85 μm based on airborne measurements // Proc. SPIE 3237, 23rd European Meeting on Atmospheric Studies by Optical Methods. 1997. – doi: 10.1117/12.284763.
- VYTOVTOV K.A., BARABANOVA E.A., GLAD-KIKH T.Y., KULINA A.L. et al. Remote Monitoring of Wa-ter Pollution With Oil Products in the Visible Range by Us-ing UAV Multispectral Camera // Proc. 2022 Int. Conf. on Information, Control, and Communication Technologies (ICCT-2022), Astrakhan, Russian Federation, 2022. P. 1–5. – doi: 10.1109/ICCT56057.2022.9976826.
- XIANG T.-Z., XIA G.-S., ZHANG L. Mini-Unmanned Aeri-al Vehicle-Based Remote Sensing: Techniques, applications, and prospects // IEEE Geoscience and Remote Sensing Magazine. 2019. Vol. 7, No. 3. P. 29–63. – doi: 10.1109/MGRS.2019.2918840.
- ZAMPIERI A., CHARPIAT G., GIRARD N., TARABAL-KA Y. Multimodal image alignment through a multiscale chain of neural networks with application to remote sensing // The European Conference on Computer Vision (ECCV). 2018. P. 657–673. – doi: 10.1007/978-3-030-01270-0_40.
- ZHANG Z., HANSON A.R. 3D Reconstruction based on homography mapping // Proc. ARPA96. 1996. P. 1007–1012.
补充文件
