Homomorphic filtering in digital multichannel image processing

User Rating:  / 0
PoorBest 

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.


повний текст / full article



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.

 

Visitors

7564436
Today
This Month
All days
3718
86922
7564436

Guest Book

If you have questions, comments or suggestions, you can write them in our "Guest Book"

Registration data

ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

Contacts

D.Yavornytskyi ave.,19, pavilion 3, room 24-а, Dnipro, 49005
Tel.: +38 (056) 746 32 79.
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
You are here: Home Archive by issue 2023 Content №3 2023 Homomorphic filtering in digital multichannel image processing