Homomorphic filtering in digital multichannel image processing
/ 0
- Details
- Category: Content №3 2023
- Last Updated on 27 June 2023
- Published on 30 November -0001
- Hits: 2546
Authors:
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.
O.V.Spirintseva, orcid.org/0000-0002-5050-5985, Oles Honchar Dnipro National University, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
V.V.Spirintsev, orcid.org/0000-0002-0908-1180, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
O.V.Kravets, orcid.org/0000-0002-3428-2232, Oles Honchar Dnipro National University, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
D.V.Spirintsev, orcid.org/0000-0001-5728-6626, Bogdan Khmelnitsky Melitopol State Pedagogical University, Zaporizhzhia, 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.
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2023, (3): 118 - 124
https://doi.org/10.33271/nvngu/2023-3/118
Abstract:
Purpose. The purpose of this article is to develop a preprocessing method for digital multispectral remote sensing images obtained through optical and infrared means in the electromagnetic spectrum. The method aims to ensure invariance with respect to positional formation conditions that determine spatial and radiometric resolution. By implementing homomorphic filtering in this method, we can significantly increase the informative value of processed imagery.
Methodology. The problem solving, including the development of the spatial and radiometric resolution increase ways for multispectral geospatial data are based on the methods of brightness spatial distribution fusion, methods of data dimension reduction, de-correlation techniques and geometric correction of image spatial distributions.
Findings. The method of preprocessing digital remote sensing data has been developed, which is a component of the methodology for identifying geometric shapes (GS) of objects in multi-channel aerospace images, allowing for a significant improvement in their recognition efficiency when noise is present.
Originality. The method of preprocessing photogrammetric scenes using homomorphic filtering to enhance their informational significance is proposed. The method ensures invariance to positional conditions of fixation, improves the accuracy of further recognition, eliminates the drawbacks of known methods associated with the existence of parametric uncertainty dependence, the features of fixation of species information, low values of information indices of synthesized images, and computational process peculiarities.
Practical value. Practical value is consists in improving of identification accuracy of objects GS in digital geospatial data, in significant increasing of raster multispectral images information value and in rising of automated image processing efficiency. The use of the method can greatly enhance the value and usefulness of multispectral photogrammetric images in a wide range of applications, from environmental monitoring to urban planning.
Keywords: object identification, geometric form, digital photogrammetric image, information value, homomorphic filtering
References.
1. Singh, P., & Shree, R. (2020). A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion. Journal of King Saud University, Computer and Information Sciences, 32, 137-148. https://doi.org/10.1016/j.jksuci.2017.06.006.
2. Kaur, H., Koundal, D., & Kadyan, V. (2021). Image Fusion Techniques: A Survey. Archives of Computational Methods in Engineering, 28, 4425-4447. https://doi.org/10.1007/s11831-021-09540-7.
3. Hnatushenko, V., Shedlovska, Y., & Shedlovsky, I. (2023). Processing Technology of Thematic Identification and Classification of Objects in the Multispectral Remote Sensing Imagery. In Babichev, S., Lytvynenko, V. (Eds.). Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_24.
4. Gamini, S., & Kumar, S.S. (2023). Homomorphic filtering for the image enhancement based on fractional-order derivative and genetic algorithm. Elsevier, Computers and Electrical Engineering, 106(2023), 108566. https://doi.org/10.1016/j.compeleceng.2022.108566.
5. Shedlovska, Y. I., & Hnatushenko, V. V. (2016). Shadow detection and removal using a shadow formation model. 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP). https://doi.org/10.1109/dsmp.2016.7583537.
6. Shedlovska, Y. I., & Hnatushenko, V. V. (2018). A Very High Resolution Satellite Imagery Classification Algorithm. IEEE 38 th International Conf. on Electronics and Nanotechnology (ELNANO), 654-657. https://doi.org/10.1109/ELNANO.2018.8477447.
7. Qu, J., Li, Y., Du, Q., Dong, W., & Xi, B. (2019). Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix. Remote Sensing, 11(9), 1005. https://doi.org/10.3390/rs11091005.
8. Prabhakar, C. J., & Kumar, P. U. (2012). An image-based technique for enhancement of underwater images. International Journal of Machine Intelligence, 3(4), 217-224. Retrieved from https://arxiv.org/abs/1212.0291.
9. Chien-Cheng, T., & Su-Ling, L. (2017). A weak-illumination image enhancement method using homomorphic filter and image fusion. Proceedings of the IEEE 6 th Global Conference on Consumer Electronics. https://doi.org/10.1109/GCCE.2017.8229192.
10. Sridevi, G., & Kumar, S.S. (2022). A Qualitative report on diffusion based image inpainting models. International Journal of Computing and Digital Systems, 11(1), 369-386. https://doi.org/10.12785/ijcds/110131.
11. Rabha, I. W., Hamid, A. J., Faten, K. K., Eatedal, A., & Nazeem, M. A. (2022). A medical image enhancement based on generalized class of fractional partial differential equations. Quantitative Imaging in Medicine and Surgery, 12(1), 172-183. https://doi.org/10.21037/qims-21-15.
12. Kaur, K., Jindal, N., & Singh, K. (2021). Fractional derivative based Unsharp masking approach for enhancement of digital images. Multimedia Tools and Applications, 80(3), 3645-3679. https://doi.org/10.1007/s11042-020-09795-5.
13. Singh, D., Nand, P., & Astya, R. (2017). Enhancement of Infrared Image with the Use of Logarithm and Entropy Functions in the Frequency Domain. International Journal of Scientific & Engineering Research, 8(12), 743-747.
14. EOSDA LandViewer: Tackling global changes with satellite data (n.d.). Retrieved from https://eos.com.
15. Mozgovoy, D., Hnatushenko, V., & Vasyliev, V. (2018). Accuracy evaluation of automated object recognition using multispectral aerial images and neural network. Proc. SPIE 10806, Tenth International Conf. on Digital Image Processing (ICDIP 2018), 108060H. https://doi.org/10.1117/12.2502905.
16. Tamta, K., Bhadauria, H. S., & Bhadauria, A. S. (2015). Object-Oriented Approach of Information Extraction from High Resolution Satellite Imagery. IOSR Journal of Computer Engineering, 3(17), 47-52. https://doi.org/10.9790/0661-17344752.
17. Long, J., Shelhamer, E., & Darrell, T. (2016). Fully Convolutional Networks for Semantic Segmentation. Computer Vision and Pattern Recognition. https://doi.org/10.48550/arXiv.1411.4038.
18. Fu, G., Liu, C., Zhou, R., Sun, T., & Zhang, Q. (2017). Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network. Remote Sensing, 5(9), 498. https://doi.org/10.3390/rs9050498.
19. Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017). Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 2(55), 645-657. https://doi.org/10.1109/TGRS.2016.2612821.
20. Maboudi, M., Amini, J., Malihi, S., & Hahn, M. (2018). Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images. ISPRS Journal of Photogrammetry and Remote Sensing, 138, 151-163. https://doi.org/10.1016/j.isprsjprs.2017.11.014.
21. Wang, C., Wang, X., Wu, D., Kuang, M., & Li, Z. (2022). Meticulous Land Cover Classification of High-Resolution Images Based on Interval Type-2 Fuzzy Neural Network with Gaussian Regression Model. Remote Sensing, 14, 3704. https://doi.org/10.3390/rs14153704.
Newer news items:
- Crowdsourcing for business strategy and sustainability: a partial least square structural equation model - 27/06/2023 03:35
- Ukraine labour potential modelling based on using the theory of unclear logic - 27/06/2023 03:35
- Analysis of motivation determinants in the implementation of online training of future managers at state universities - 27/06/2023 03:35
- Financial and credit support of market-oriented management of transport engineering enterprises - 27/06/2023 03:35
- Development of collective intelligence in the enterprises’ digital transformation - 27/06/2023 03:35
- Fundamental imperatives of eliminating uncertainty on the basis of monitoring the activity of the iron ore enterprise - 27/06/2023 03:35
- Experimental research on muffle furnace dynamic properties - 27/06/2023 03:35
- Evaluation and control of data relevance in information systems of the transport industry management - 27/06/2023 03:35
- Optimization of track distribution of industrial railway stations between car designations - 27/06/2023 03:35
- Methodology for assessing the influence of external and internal factors on the development of the digital economy - 27/06/2023 03:35
Older news items:
- Economic aspects of assessment and marketing of carbon emissions by enterprises based on the principles of sustainable development - 27/06/2023 03:35
- Problem issues regarding legal liability for environmental offenses in Ukraine - 27/06/2023 03:35
- Mathematical model of air flow movement in a motorized filter respirator - 27/06/2023 03:35
- Methods for improving accounting and analytical support of enterprises in order to protect the environment - 27/06/2023 03:35
- Assessment of the risk to human health from the accumulation of heavy metals in and around the “2 July” neighborhood (Mitrovica-Kosovo, Kosovo) - 27/06/2023 03:35
- Assessing the accuracy of modeling the tubbing erector manipulator mechanism in solidworks motion program - 27/06/2023 03:35
- Long-term thermal productivity of polystyrene concrete in a new composite wall in a fixed formwork - 27/06/2023 03:35
- Design of double-shoulder threaded joints of drilling string elements - 27/06/2023 03:35
- Microstructures and mechanical properties of cold rolled pipes with increased small deformation - 27/06/2023 03:35
- Converting Slovianska TPP with the central coal pulverizing plant from anthracite to sub-bituminous coal - 27/06/2023 03:35
Archive