Determination of the number of clusters on images from space optic-electronic observation systems using the k-means algorithm
DOI:
https://doi.org/10.15587/1729-4061.2023.282374Keywords:
image clustering, space observation system, k-means, errors of the 1st and 2nd kind, number of clustersAbstract
The object of research is the process of clustering images from space optical-electronic surveillance systems. The main hypothesis of the study assumed that experimental studies would make it possible to determine the number of clusters on images from space optical-electronic surveillance systems when using the k-means algorithm.
The method of clustering images from space optical-electronic surveillance systems using the k-means algorithm, unlike the known ones, implies:
– splitting the source image into Red-Green-Blue brightness channels;
– determination of the Euclidean distance between pixels;
– distribution of the entire set of image pixels into clusters;
– recalculation of "centers" of each subset;
– reassignment of new "centers" of each cluster;
– minimization of the total intracluster variance.
Experimental studies were conducted on the clustering of the original image using the k-means method at different values of k. It was established that with an increase in the value of k, the visual quality of clustering improves, and it is possible to visually determine a larger number of clusters in the images.
To determine the number of clusters, the sum of clustering errors of type 1 and 2 at different values of k was evaluated. It was established that when the value of k increases, the sum of errors of the 1st and 2nd kind initially decreases exponentially. A further increase in the value of k does not lead to a significant decrease in errors of the 1st and 2nd kind. It was established that for a typical image from the space optical-electronic observation system, the value of k in the clustering method based on the k-means algorithm should be equal to 4. At the same time, the sum of errors of the 1st and 2nd kind is 31.3 %.
Further research is directed to the development of clustering methods that reduce the sum of errors of the 1st and 2nd kind
References
- Green, M. (2020). K-Means Clustering for Surface Segmentation of Satellite Images. Available at: https://medium.com/@maxfieldeland/k-means-clustering-for-surface-segmentation-of-satellite-images-ad1902791ebf
- Lafabregue, B., Gancarski, P., Weber, J., Forestier, G. (2022). Incremental constrained clustering with application to remote sensing images time series. 2022 IEEE International Conference on Data Mining Workshops (ICDMW). doi: https://doi.org/10.1109/icdmw58026.2022.00110
- Lampert, T., Lafabregue, B., Dao, T.-B.-H., Serrette, N., Vrain, C., Gancarski, P. (2019). Constrained Distance-Based Clustering for Satellite Image Time-Series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12 (11), 4606–4621. doi: https://doi.org/10.1109/jstars.2019.2950406
- Space, the unseen frontier in the war in Ukraine (2022). BBC News. Available at: https://www.bbc.com/news/technology-63109532
- Khudov, H., Makoveichuk, O., Khizhnyak, I., Shamrai, B., Glukhov, S., Lunov, O. et al. (2022). The Method for Determining Informative Zones on Images from On-Board Surveillance Systems. International Journal of Emerging Technology and Advanced Engineering, 12 (8), 61–69. doi: https://doi.org/10.46338/ijetae0822_08
- Samanta, S., Chatterjee, S. (2018). A Survey On Data Clustering Approaches. 1st International Business Research Conference (IBRC 2018), 34–42. Available at: https://www.researchgate.net/publication/341134327_A_Survey_On_Data_Clustering_Approaches
- Pandey, S., Khanna, P. (2014). A hierarchical clustering approach for image datasets. 2014 9th International Conference on Industrial and Information Systems (ICIIS). doi: https://doi.org/10.1109/iciinfs.2014.7036504
- Aktas, Y. C. (2021). Image Segmentation with Clustering. The Fundamentals of K-Means and Fuzzy-C Means Clustering and their usage for Image Segmentation. Towards Data Science. – 2021. Available at: https://towardsdatascience.com/image-segmentation-with-clustering-b4bbc98f2ee6
- Dhanachandra, N., Manglem, K., Chanu, Y. J. (2015). Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm. Procedia Computer Science, 54, 764–771. doi: https://doi.org/10.1016/j.procs.2015.06.090
- Funmilola, A. A., Oke, O. A., Adedeji, T. O., Alade, O. M., Adewusi, E. A. (2012). Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation. Journal of Information Engineering and Applications, 2 (6), 21–33. Available at: https://core.ac.uk/download/pdf/234676965.pdf
- Kishor Duggirala, R. (2020). Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms. Introduction to Data Science and Machine Learning. doi: https://doi.org/10.5772/intechopen.86374
- NamAnh, D. (2015). Segmentation by Incremental Clustering. International Journal of Computer Applications, 111 (12), 23–30. doi: https://doi.org/10.5120/19591-1360
- Niharika, E., Adeeba, H., Krishna, A. S. R., Yugander, P. (2017). K-means based noisy SAR image segmentation using median filtering and otsu method. 2017 International Conference on IoT and Application (ICIOT). doi: https://doi.org/10.1109/iciota.2017.8073630
- Zheng, X., Lei, Q., Yao, R., Gong, Y., Yin, Q. (2018). Image segmentation based on adaptive K-means algorithm. EURASIP Journal on Image and Video Processing, 2018 (1). doi: https://doi.org/10.1186/s13640-018-0309-3
- Hess, T., Sabato, S. (2020). Sequential no-Substitution k-Median-Clustering. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, 962–972. Available at: https://proceedings.mlr.press/v108/hess20a.html
- Shah, N., Patel, D., Fränti, P. (2021). k-Means image segmentation using Mumford–Shah model. Journal of Electronic Imaging, 30 (06). doi: https://doi.org/10.1117/1.jei.30.6.063029
- Wang, C., Pedrycz, W., Li, Z., Zhou, M., Ge, S. S. (2021). G-Image Segmentation: Similarity-Preserving Fuzzy C-Means With Spatial Information Constraint in Wavelet Space. IEEE Transactions on Fuzzy Systems, 29 (12), 3887–3898. doi: https://doi.org/10.1109/tfuzz.2020.3029285
- Khosla, R. (2020). An Approach towards Neural Network based Image Clustering. Analytics Vidhya. Available at: https://www.analyticsvidhya.com/blog/2020/12/an-approach-towards-neural-network-based-image-clustering/
- Li, H., Li, J., Zhu, M. (2023). End-to-end unsupervised clustering neural networks for image clustering. doi: https://doi.org/10.36227/techrxiv.22147559.v2
- Guérin, J., Boots, B. (2018). Improving Image Clustering With Multiple Pretrained CNN Feature Extractors. Available at: https://homes.cs.washington.edu/~bboots/files/GuerinBMVC18.pdf
- Benito-Picazo, J., Palomo, E. J., Dominguez, E., Ramos, A. D. (2020). Image Clustering Using a Growing Neural Gas with Forbidden Regions. 2020 International Joint Conference on Neural Networks (IJCNN). doi: https://doi.org/10.1109/ijcnn48605.2020.9207700
- Zhang, L.-F., Li, C.-F., Wang, H.-R., Shi, M.-Y. (2018). Research On Face Image Clustering Based On Integrating Som And Spectral Clustering Algorithm. 2018 International Conference on Machine Learning and Cybernetics (ICMLC). doi: https://doi.org/10.1109/icmlc.2018.8526946
- Ke, S., Zhao, Y., Li, B., Wu, Z., Liu, X. (2016). Fast image clustering based on convolutional neural network and binary K-means. Eighth International Conference on Digital Image Processing (ICDIP 2016). doi: https://doi.org/10.1117/12.2244263
- Al-Qaisi, L., Hassonah, M. A., Al-Zoubi, M. M., Al-Zoubi, A. M. (2021). A Review of Evolutionary Data Clustering Algorithms for Image Segmentation. Algorithms for Intelligent Systems, 201–214. doi: https://doi.org/10.1007/978-981-33-4191-3_9
- Abeysinghe, W., Wong, M., Hung, C.-C., Bechikh, S. (2019). Multi-Objective Evolutionary Algorithm for Image Segmentation. 2019 SoutheastCon. doi: https://doi.org/10.1109/southeastcon42311.2019.9020457
- Khudov, H., Makoveichuk, O., Butko, I., Gyrenko, I., Stryhun, V., Bilous, O. et al. (2022). Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm. Eastern-European Journal of Enterprise Technologies, 3 (9 (117)), 6–14. doi: https://doi.org/10.15587/1729-4061.2022.259759
- Ruban, I., Khudov, H., Makoveichuk, O., Khudov, V., Kalimulin, T., Glukhov, S. et al. (2022). Methods of UAVs images segmentation based on k-means and a genetic algorithm. Eastern-European Journal of Enterprise Technologies, 4 (9 (118)), 30–40. doi: https://doi.org/10.15587/1729-4061.2022.263387
- Ruban, I., Khudov, H., Makoveichuk, O., Butko, I., Glukhov, S., Khizhnyak, I. et al. (2022). Application of the Particle Swarm Algorithm to the Task of Image Segmentation for Remote Sensing of the Earth. Lecture Notes in Networks and Systems, 573–585. doi: https://doi.org/10.1007/978-981-19-5845-8_40
- Khudov, H., Makoveichuk, O., Khizhnyak, I., Oleksenko, O., Khazhanets, Y., Solomonenko, Y. et al. (2022). Devising a method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm. Eastern-European Journal of Enterprise Technologies, 2 (9 (116)), 6–13. doi: https://doi.org/10.15587/1729-4061.2022.255203
- Khudov, H., Makoveichuk, O., Khudov, V., Maliuha, V., Andriienko, A., Tertyshnik, Y. et al. (2022). Devising a method for segmenting images acquired from space optical and electronic observation systems based on the Sine-Cosine algorithm. Eastern-European Journal of Enterprise Technologies, 5 (9 (119)), 17–24. doi: https://doi.org/10.15587/1729-4061.2022.265775
- 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
- Satellite Imagery. Available at: https://www.maxar.com/products/satellite-imagery
- Khudov, G. V. (2003). Features of optimization of two-alternative decisions by joint search and detection of objects. Problemy Upravleniya I Informatiki (Avtomatika), 5, 51–59. Available at: https://www.researchgate.net/publication/291431400_Features_of_optimization_of_two-alternative_decisions_by_joint_search_and_detection_of_objects
- Khudov, H., Makoveichuk, O., Misiuk, D., Pievtsov, H., Khizhnyak, I., Solomonenko, Y. et al. (2022). Devising a method for processing the image of a vehicle's license plate when shooting with a smartphone camera. Eastern-European Journal of Enterprise Technologies, 1 (2 (115)), 6–21. doi: https://doi.org/10.15587/1729-4061.2022.252310
- Khudov, H., Makoveichuk, O., Khizhnyak, I., Glukhov, S., Shamrai, N., Rudnichenko, S. et al. (2022). The Choice of Quality Indicator for the Image Segmentation Evaluation. International Journal of Emerging Technology and Advanced Engineering, 12 (10), 95–103. doi: https://doi.org/10.46338/ijetae1022_11
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Copyright (c) 2023 Hennadii Khudov, Oleksandr Makoveichuk, Volodymyr Komarov, Vladyslav Khudov, Irina Khizhnyak, Volodymyr Bashynskyi, Stanislav Stetsiv, Yevhen Dudar, Andrii Rudiy, Mykhailo Buhera
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