Multilevel thresholding Aerial image segmentation using comprehensive learning-based Snow ablation optimizer with double attractors

Authors

  • Mohamed Abd Elaziz Emirates International University image/svg+xml Author https://orcid.org/0000-0002-7682-6269
  • Mohammed A.A. Al-qaness Emirates International University image/svg+xml Author
  • Rehab Ali Ibrahim Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt Author
  • Ahmed A. Ewees Department of Computer, Damietta University, Damietta 34517, Egypt Author
  • Mansour Shrahili Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia Corresponding author at: Department of Computer, Damietta University, Damietta 34517, Egypt. Author https://orcid.org/0000-0003-3456-8393

DOI:

https://doi.org/10.1016/j.eij.2024.100500

Keywords:

Aerial image segmentation , Snow ablation optimizer , Comprehensive learning , Double attractors

Abstract

Aerial photography is a remote sensing technique used for target detection, enabling both qualitative and quantitative analysis. The segmentation process is considered one of the most important processes to improve the analysis of Aerial images. In this study, we introduce an alternative multilevel threshold image segmentation based on a modified Snow ablation optimizer (SAO) algorithm. This modification is conducted using the strengths of Comprehensive learning and Double attractors which aims to enhance the exploration and exploitation abilities of the SAO during the process of discovering the optimal threshold levels that are used to segment the Aerial photography image. To validate the quality of the modified version of SAO, named DCSAO, a set of experimental series is conducted using the CEC2022 benchmark function and sixteen Aerial images at different threshold levels. In addition, we compared the results of DCSAO with different well-known Metaheuristic techniques. The results show the superior performance of DCSAO in comparison to other algorithms according to the performance metrics.

Author Biography

  • Mohammed A.A. Al-qaness, Emirates International University
    Mohammed A.A. Al-qaness

    View in Scopus

    College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China   Zhejiang Institute of Optoelectronics, Jinhua 321004, China   College of Engineering and Information Technology, Emirates International University, Sana’a 16881, Yemen   Corresponding author at: College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China. alqaness@zjnu.edu.cn

References

Aalan Babu, A., & Mary Anita Rajam, V. (2020). Water-body segmentation from satellite images using Kapur's entropy-based thresholding method. Computational Intelligence, 36(3), 1242–1260. https://doi.org/10.1111/coin.12260

Abd Elaziz, M., Heidari, A. A.,固定固定固定 fixedFujita, H., & Moayedi, H. (2020). A competitive chain-based harris hawks optimizer for global optimization and multi-level image thresholding problems. Applied Soft Computing, 95, Article 106347. https://doi.org/10.1016/j.asoc.2020.106347

Abualigah, L.,固定固定固定 fixed固定固定固定 fixed固定固定 fixedAbd Elaziz, M., Sumari, P., Geem, Z. W., & Gandomi, A. H. (2022). Reptile search algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 191, Article 116158. https://doi.org/10.1016/j.eswa.2021.116158

Abualigah, L., Al-Okbi, N. K., Elaziz, M. A., & Houssein, E. H. (2022). Boosting marine predators algorithm by salp swarm algorithm for multilevel thresholding image segmentation. Multimedia Tools and Applications, 81(12), 16707–16742. https://doi.org/10.1007/s11042-022-11901-8

Abualigah, L., Almotairi, K. H., & Elaziz, M. A. (2023). Multilevel thresholding image segmentation using meta-heuristic optimization algorithms: Comparative analysis, open challenges and new trends. Applied Intelligence, 53(10), 11654–11704. https://doi.org/10.1007/s10489-022-03972-9

Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, Article 114570. https://doi.org/10.1016/j.cma.2022.114570

Al-Alimi, D., et al. (2022). Compression and reinforce variation with convolutional neural networks for hyperspectral image classification. Applied Soft Computing, 116, Article 108307. https://doi.org/10.1016/j.asoc.2021.108307

Al-Alimi, D., et al. (2023a). ETR: Enhancing transformation reduction for reducing dimensionality and classification complexity in hyperspectral images. Expert Systems with Applications, 215, Article 119330. https://doi.org/10.1016/j.eswa.2022.119330

Al-Alimi, D., et al. (2023b). IDA: Improving distribution analysis for reducing data complexity and dimensionality in hyperspectral images. Pattern Recognition, 138, Article 109355. https://doi.org/10.1016/j.patcog.2023.109355

Al-qaness, M. A., Ewees, A. A., Abd Elaziz, M., Dahou, A., Al-Betar, M. A., Aseeri, A. O., & et al. (2023). Boosted barnacles algorithm optimizer: Comprehensive analysis for social IoT applications. IEEE Access, 11, 43155–43182. https://doi.org/10.1109/ACCESS.2023.3245519

Awad, N. H., Ali, M. Z., & Suganthan, P. N. (2017). Ensemble sinusoidal differential covariance matrix adaptation with euclidean neighborhood for solving CEC2017 benchmark problems. In 2017 IEEE Congress on Evolutionary Computation (CEC) (pp. 372–379). IEEE. https://doi.org/10.1109/CEC.2017.7969343

Bhandari, A. K. (2020). A noble beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation. Neural Computing and Applications, 32(9), 4583–4613. https://doi.org/10.1007/s00521-019-04446-y

Bhatti, U. A., Zhou, M. Q., Huo, Q. S., Ali, S., Hussain, A., & Yan, Y. (2021). Advanced color edge detection using clifford algebra in satellite images. IEEE Photonics Journal, 13(2), 1–20. https://doi.org/10.1109/JPHOT.2021.3061112

Cantorna, D., Dafonte, C., Iglesias, A., & Arcay, B. (2019). Oil spill segmentation in SAR images using convolutional neural networks: A comparative analysis with clustering and logistic regression algorithms. Applied Soft Computing, 84, Article 105716. https://doi.org/10.1016/j.asoc.2019.105716

Daoud, M. S., Shehab, M., Abualigah, L., Alshinwan, M., Elaziz, M. A., Shambour, M. K. Y., & et al. (2023). Recent advances of chimp optimization algorithm: Variants and applications. Journal of Bionic Engineering, 20(4), 1–23. https://doi.org/10.1007/s42235-023-00337-y

Deng, L., &固定固定 fixedLiu, S. (2023). Snow ablation optimizer: A novel metaheuristic technique for numerical optimization and engineering design. Expert Systems with Applications, 225, Article 120069. https://doi.org/10.1016/j.eswa.2023.120069

Edwards, A. C., Scalenghe, R., & Freppaz, M. (2007). Changes in the seasonal snow cover of alpine regions and its effect on soil processes: A review. Quaternary International, 162, 172–181. https://doi.org/10.1016/j.quaint.2006.10.027

Eisham, Z. K., Haque, M. M., Rahman, M. S., Nishat, M. M., Faisal, F., & Islam, M. R. (2023). Chimp optimization algorithm in multilevel image thresholding and image clustering. Evolutionary Intelligence, 14(4), 605–648. https://doi.org/10.1007/s12065-022-00777-w

Ewees, A. A., Abd Elaziz, M., Al-Qaness, M. A. A.,固定 fixed固定 fixed固定 fixedKhalil, H. A., & Kim, S. (2020). Improved artificial bee colony using sine-cosine algorithm for multi-level thresholding image segmentation. IEEE Access, 8, 26304–26315. https://doi.org/10.1109/ACCESS.2020.2970485

Ewees, A. A., Abd Elaziz, M., & Oliva, D. (2018). Image segmentation via multilevel thresholding using hybrid optimization algorithms. Journal of Electronic Imaging, 27(6), Article 063008. https://doi.org/10.1117/1.JEI.27.6.063008

Gallego, A.-J., Pertusa, A., & Gil, P. (2018). Automatic ship classification from optical aerial images with convolutional neural networks. Remote Sensing, 10(4), Article 511. https://doi.org/10.3390/rs10040511

Griffiths, D., & Boehm, J. (2019). Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours. ISPRS Journal of Photogrammetry and Remote Sensing, 154, 70–83. https://doi.org/10.1016/j.isprsjprs.2019.05.012

Hatamizadeh, A., Sengupta, D., & Terzopoulos, D. (2020). End-to-end trainable deep active contour models for automated image segmentation: Delineating buildings in aerial imagery. In Computer Vision – ECCV 2020 (pp. 730–746). Springer. https://doi.org/10.1007/978-3-030-58517-4_43

He, G., & Lu, X. (2022). Good point set and double attractors based-QPSO and application in portfolio with transaction fee and financing cost. Expert Systems with Applications, 209, Article 118339. https://doi.org/10.1016/j.eswa.2022.118339

He, L., & Huang, S. (2020). An efficient krill herd algorithm for color image multilevel thresholding segmentation problem. Applied Soft Computing, 89, Article 106063. https://doi.org/10.1016/j.asoc.2020.106063

Iqbal, M. S., Ali, H., Tran, S. N., & Iqbal, T. (2021). Coconut trees detection and segmentation in aerial imagery using mask region-based convolution neural network. IET Computer Vision, 15(6), 428–439. https://doi.org/10.1049/cvi2.12046

Kavzoglu, T., & Tonbul, H. (2017). A comparative study of segmentation quality for multi-resolution segmentation and watershed transform. In 2017 8th International Conference on Recent Advances in Space Technologies (RAST) (pp. 113–117). IEEE. https://doi.org/10.1109/RAST.2017.8002988

Kumar, A.,固定固定 fixedKumar, A., Vishwakarma, A., & Singh, G. K. (2022). Multilevel thresholding for crop image segmentation based on recursive minimum cross entropy using a swarm-based technique. Computers and Electronics in Agriculture, 203, Article 107488. https://doi.org/10.1016/j.compag.2022.107488

Kurban, R., Durmus, A., & Karakose, E. (2021). A comparison of novel metaheuristic algorithms on color aerial image multilevel thresholding. Engineering Applications of Artificial Intelligence, 105密, Article 104410. https://doi.org/10.1016/j.engappai.2021.104410

Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295. https://doi.org/10.1109/TEVC.2005.857612

Liu, N., Guo, B., Li, X., & Min, X. (2021). Gradient clustering algorithm based on deep learning aerial image detection. Pattern Recognition Letters, 141, 37–44. https://doi.org/10.1016/j.patrec.2020.10.016

Lou, C., Al-qaness, M. A. A., AL-Alimi, D., Dahou, A., Abd Elaziz, M., Abualigah, L., & et al. (2024). Land use/land cover (LULC) classification using hyperspectral images: A review. Geo-spatial Information Science, 1–42. https://doi.org/10.1080/10095020.2023.2285431

Mahajan, S., & Pandit, A. K. (2022). Image segmentation and optimization techniques: A short overview. Medicon Engineering Themes, 2(2), 47–49. https://doi.org/10.55162/MCET.02.021

Marmanis, D., Schindler, K., Wegner, J. D., Galliani, S., Datcu, M., & Stilla, U. (2018). Classification with an edge: Improving semantic image segmentation with boundary detection. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 158–172. https://doi.org/10.1016/j.isprsjprs.2017.11.009

Moussa, M., Guedri, W., &固定 Douik, A. (2020). A novel metaheuristic algorithm for edge detection based on artificial bee colony. Traitement du Signal, 37(3), 405–412. https://doi.org/10.18280/ts.370308

Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076

Popescu, D., & Ichim, L. (2015). Image recognition in UAV application based on texture analysis. In Advanced Concepts for Intelligent Vision Systems (ACIVS 2015) (pp. 693–704). Springer. https://doi.org/10.1007/978-3-319-25903-1_60

Popescu, D., & Ichim, L. (2016). Aerial image segmentation by use of textural features. In 2016 20th International Conference on System Theory, Control and Computing (ICSTCC) (pp. 721–726). IEEE. https://doi.org/10.1109/ICSTCC.2016.7788151

Qin, H., Ke, L., Ma, X., Danelljan, M.,固定 fixedTai, Y.-W., Tang, C.-K., & et al. (2024). Bimatting: Efficient video matting via binarization. Advances in Neural Information Processing Systems, 36, 5410–5424.

Qin, H., Zhang, X.,固定 fixedGong, R., Ding, Y., Xu, Y., & Liu, X. (2023a). Distribution-sensitive information retention for accurate binary neural network. International Journal of Computer Vision, 131(1), 26–47. https://doi.org/10.1007/s11263-022-01696-9

Qin, H., Ding, Y., Zhang, X., Wang, J., Liu, X., & Lu, J. (2023b). Diverse sample generation: Pushing the limit of generative data-free quantization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 13424–13438. https://doi.org/10.1109/TPAMI.2023.3289514

Qin, H., Zhang, Y., Ding, Y., Liu, X., Danelljan, M., Yu, F., & et al. (2024b). QuantSR: Accurate low-bit quantization for efficient image super-resolution. Advances in Neural Information Processing Systems, 36, 5122–5136.

Rahkar Farshi, T., & K. Ardabili, A. (2021). A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding. Multimedia Systems, 27(1), 125–142. https://doi.org/10.1007/s00530-020-00714-y

Rai, R., Das, A., &固定 Dhal, K. G. (2022). Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: An inclusive review. 固定Evolutionary Systems, 13(6), 889–945. https://doi.org/10.1007/s12065-021-00602-8

Sağ, T., & Çunkaş, M. (2015). Color image segmentation based on multiobjective artificial bee colony optimization. Applied Soft Computing, 34, 389–401. https://doi.org/10.1016/j.asoc.2015.05.016

Shamsoshoara, A., Afghah, F., Razi, A., Zheng, L., Fulé, P. Z., & Blasch, E. (2021). Aerial imagery pile burn detection using deep learning: The FLAME dataset. Computer Networks, 193, Article 108001. https://doi.org/10.1016/j.comnet.2021.108001

Sharma, N., Scully-Power, P., & Blumenstein, M. (2018). Shark detection from aerial imagery using region-based CNN, a study. In AI 2018: Advances in Artificial Intelligence (pp. 224–236). Springer. https://doi.org/10.1007/978-3-030-03991-2_22

Thanh, L. T., & Thanh, D. N. H. (2020). An adaptive local thresholding roads segmentation method for satellite aerial images with normalized HSV and Lab color models. In Intelligent Computing in Engineering (pp. 865–872). Springer. https://doi.org/10.1007/978-981-15-0751-9_108

Vorotyntsev, P., Gordienko, Y., Alienin, O., Rokovyi, O., & Stirenko, S. (2021). Satellite image segmentation using deep learning for deforestation detection. In 2021 IEEE 3rd Ukraine Conference on Electrical and Computer Engineering (UKRCON) (pp. 226–231). IEEE. https://doi.org/10.1109/UKRCON53538.2021.9545557

Xing, Z. (2020). An improved emperor penguin optimization based multilevel thresholding for color image segmentation. Knowledge-Based Systems, 194, Article 105570. https://doi.org/10.1016/j.knosys.2020.105570

Xu, L.,固定固定 fixedJia, H., Lang, C., Peng, X., & Sun, K. (2019). A novel method for multilevel color image segmentation based on dragonfly algorithm and differential evolution. IEEE Access, 7, 19502–19538. https://doi.org/10.1109/ACCESS.2019.2896593

Yuan, J., Gleason, S. S., & Cheriyadat, A. M. (2013). Systematic benchmarking of aerial image segmentation. IEEE Geoscience and Remote Sensing Letters, 10(6), 1527–1531. https://doi.org/10.1109/LGRS.2013.2243719

Zhang, J., Feng, W., Hu, C., &固定 Luo, Y. (2017). Image segmentation method for forestry unmanned aerial vehicle pest monitoring based on composite gradient watershed algorithm. Transactions of the Chinese Society of Agricultural Engineering, 33(14), 93–99. https://doi.org/10.11975/j.issn.1002-6819.2017.14.013

Zhang, Y., Gao, X., Duan, Q., Yuan, L., &固定 Gao, X. (2022). DHT: Deformable hybrid transformer for aerial image segmentation. IEEE Geoscience and Remote Sensing Letters, 19, 1–5. https://doi.org/10.1109/LGRS.2021.3061145

固定Zhao, X., Turk, M., Li, W., Lien, K.-C., & Wang, G. (2016). A multilevel image thresholding segmentation algorithm based on two-dimensional K–L divergence and modified particle swarm optimization. Applied Soft Computing, 48, 151–159. https://doi.org/10.1016/j.asoc.2016.06.023

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2024-01-19

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Abd Elaziz, M., Al-qaness, M. A., Ibrahim, R. A., Ewees, A. A., & Shrahili, M. (2024). Multilevel thresholding Aerial image segmentation using comprehensive learning-based Snow ablation optimizer with double attractors. Emirates International University Digital Repository, 1(1). https://doi.org/10.1016/j.eij.2024.100500

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