Науковий вісник НГУ

Intelligent Sentinel satellite image processing technology for land cover mapping

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №5 2024

Last Updated on 29 October 2024

Published on 30 November -0001

Hits: 1531

Authors:

V.Yu.Kashtan, orcid.org/0000-0002-0395-5895, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.V.Hnatushenko*, orcid.org/0000-0003-3140-3788, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.S.Laktionov, orcid.org/0000-0001-7857-6382, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

H.H.Diachenko, orcid.org/0000-0001-9105-1951, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (5): 143 - 150

https://doi.org/10.33271/nvngu/2024-5/143

Abstract:

Purpose. This article proposes to develop an intelligent Sentinel satellite image processing technology for land cover mapping using convolutional neural networks. The result will be an image with improved spatial resolution.

Methodology. The paper presents a technology using a combination of biquadratic interpolation, histogram alignment, PCA transform, as well as a parallel residual architecture of convolutional neural networks. The technology increases the information content of Sentinel-2 optical images by combining 10 and 20-meter resolution data, resulting in primary 20-meter images with improved spatial resolution.

Findings. The root mean square error (RMSE = 3.64) indicates a high accuracy in reproducing the spectral properties of the images. The correlation coefficient (CC = 0.997) confirms a high linear relationship between the estimated and observed images. The low value of Spectral Angle Mapper (SAM = 0.52) with the high Universal Image Quality Index (UIQI = 0.999) indicates high quality and structural similarity between the synthesized and reference images. These results confirm the proposed technology’s effectiveness in enhancing the spatial resolution of Sentinel satellite images.

Originality. Traditional pansharpening methods of multispectral images developed for satellite images with panchromatic channels cannot be directly applied to Sentinel multispectral data, because these images do not contain a panchromatic channel. In addition, atmospheric conditions and the presence of clouds affect the quality of optical images, complicating their further thematic processing. The proposed technology, using biquadratic interpolation, histogram alignment, convolutional neural networks, and PCA transformation, removes clouds and enhances the spatial resolution of the primary 20-meter optical satellite image channels of Sentinel-2. This technology reduces color distortion and increases the detail of digital optical images, which allows for more accurate analysis of the state of the earth’s surface.

Practical value. The results obtained can be used to improve the methods for processing Sentinel satellite images, which provide high spatial resolution and accurate preservation of spectral characteristics. It provides the foundation for the development of new geographic information systems for land cover monitoring.

Keywords: convolutional neural network, image, remote sensing, spatial resolution

References.

1. Griffiths, P., Nendel, C., & Hostert, P. (2019). Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sensing of Environment, 220, 135-151. https://doi.org/10.1016/j.rse.2018.10.031.

2. Gerland, P., Hertog, S., Wheldon, M., Kantorova, V., Gu, D., Gonnella, G., …, & Spoorenberg, T. (2022). World Population Prospects 2022: Summary of results.

3. Pawlak, K., & Kołodziejczak, M. (2020). The Role of Agriculture in Ensuring Food Security in Developing Countries: Considerations in the Context of the Problem of Sustainable Food Production. Sustainability, 12, 5488. https://doi.org/10.3390/su12135488.

4. Dudin, V., Polehenka, M., Tkalich, O., Pavlychenko, A., Hapich, H., & Roubík, H. (2024). Ecological and economic assessment of the effectiveness of implementing bioenergy technologies in the conditions of post-war recovery of Ukraine. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (1), 203-208. https://doi.org/10.33271/nvngu/2024-1/203.

5. Löw, F., & Duveiller, G. (2014). Defining the spatial resolution requirements for crop identification using optical remote sensing. Remote Sensing, 6, 9034-9063. https://doi.org/10.3390/rs6099034.

6. Hnatushenko, V. V., Sierikova, K. Y., & Sierikov, I. Y. (2018). Development of a Cloud-Based Web Geospatial Information System for Agricultural Monitoring Using Sentinel-2 Data. 2018 IEEE 13^th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). Lviv, Ukraine, 2018, (pp. 270-273). https://doi.org/10.1109/STC-CSIT.2018.8526717.

7. Beylkin, G., Monzón, L., & Satkauskas, I. (2019). On computing distributions of products of random variables via Gaussian multiresolution analysis. Applied and Computational Harmonic Analysis, 47, 306-337. https://doi.org/10.1016/j.acha.2017.08.008.

8. Sugumar, R., & Suganya, D. (2023). A multi-spectral image-based high-level classification based on a modified SVM with enhanced PCA and hybrid metaheuristic algorithm. Remote Sensing Applications: Society and Environment, 31. https://doi.org/10.1016/j.rsase.2023.100984.

9. Guo, Q., Liu, Z., & Liu, S. (2010). Color image encryption by using Arnold and discrete fractional random transforms in IHS space. Optics and Lasers in Engineering, 48, 1174-1181. https://doi.org/10.1016/j.optlaseng.2010.07.005.

10. Carson, E., Lund, K., Rozložník, M., & Thomas, S. (2022). Block Gram-Schmidt algorithms and their stability properties. Linear Algebra and its Applications, 638, 150-195. https://doi.org/10.1016/j.laa.2021.12.017.

11. Kashtan, V., & Hnatushenko, V. (2020). A wavelet and HSV pansharpening technology of high resolution satellite images. 1^st International Workshop on Intelligent Information Technologies & Systems of Information Security (IntelITSIS-2020), Khmelnytskyi, Ukraine June 10-12, 2020.

12. Massout, S., & Smara, Y. (2021). Panchromatic and multispectral image fusion using the spatial frequency and the à trous wavelet transform. Journal of Applied Remote Sensing, 15. https://doi.org/10.1117/1.JRS.15.036510.

13. Kaplan, G. (2018). Sentinel-2 Pan Sharpening-Comparative Ana­ly­sis. MDPI Proceedings, 2, 345. https://doi.org/10.3390/ecrs-2-05158.

14. Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. (2016). Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing, 8, 354. https://doi.org/10.3390/rs8040354.

15. Lanaras, C., Bioucas-Dias, J.M., Galliani, S., Baltsavias, E., & Schindler, K. (2018). Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network. ISPRS Journal of Photogrammetry and Remote Sensing, V.146, 305-319. https://doi.org/10.1016/j.isprsjprs.2018.09.018.

16. Yuan, Q., Wei, Y., Meng, X., Shen, H., & Zhang, L. (2018). A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 11, 978-989. https://doi.org/10.1109/JSTARS.2018.2794888.

17. Shao, Z., Cai, J., Fu, P., Hu, L., & Lui, T. (2019). Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product. Remote Sensing of Environment, 235, 111425. https://doi.org/10.1016/j.rse.2019.111425.

18. Zhang, C., & Li, X. (2022). Land Use and Land Cover Mapping in the Era of Big Data. Land, 11, 1692. https://doi.org/10.3390/land11101692.

19. Hnatushenko, V., Kogut, P., & Uvarov, M. (2022). Variational approach for rigid co-registration of optical/SAR satellite images in agricultural areas. Journal of Computational and Applied Mathematics, 400, 113742. https://doi.org/10.1016/j.cam.2021.113742.

20. Khanna, R., Sa, I., Nieto, J., & Siegwart, R. (2017). On field radiometric calibration for multispectral cameras, 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, (pp. 6503-6509). https://doi.org/10.1109/ICRA.2017.7989768.

21. Hnatushenko, V., Hnatushenko, Vik., Kashtan, V., Reuta, O., & Udovyk, I. (2021). Voxel Approach to the Shadow Formation Process in Image Analysis. 11^th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 22-25 September, 2021, Cracow, Poland, (pp. 33-36). https://doi.org/10.1109/IDAACS53288.2021.9660909.

22. Lim, B., Son, S., Kim, H., Nah, S., & Mu Lee, K. (2017). Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, (pp. 136-144). https://doi.org/10.48550/arXiv.1707.02921.

23. Rozenhaimer, M.S., Li, A., Das, K., & Chirayath, V. (2020). Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN). Remote Sensing of Environment, 237, 111446. https://doi.org/10.1016/j.rse.2019.111446.

24. Loncan, L., De Almeida, L.B., Bioucas-Dias, J.M., Briottet, X., Chanussot, J., Dobigeon, N., …, & Yokoya, N. (2015). Hyperspectral pansharpening: A review. IEEE Geoscience and Remote Sensing Magazine, 3(3), 27-46. https://doi.org/10.1109/MGRS.2015.2440094.

• < Prev
• Next >