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Optimizing synthetic aperture radar image classification and change detection: a proportional factor firefly algorithm and multilayer perceptron approach

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Abstract

Change detection in remote sensing images is essential for monitoring alterations in the Earth’s surface over time. Despite numerous methodologies suggested for synthetic aperture radar (SAR) image classification and change detection, achieving both high accuracy and computational efficiency remains a challenge. To address this gap, present the proportional factor-based firefly optimization and multilayer perceptron deep neural network (PFFO-MPDNN) framework tailored for SAR image classification and change detection (SAR-IC-CD). This approach begins by harnessing data from diverse geographical locations, including Bern, Ottawa, Mexico, and the Yellow River datasets. employing bilateral and spatial filtering (BSF) for noise reduction and modified contrast-limited adaptive histogram equalization (MCLAHE) for contrast enhancement, prepare the data for subsequent analysis. Leveraging the modified gauss log ratio (MGLR) operator, derive dissimilar image representations, enriching the dataset’s discriminative power. Next, extract features using ternary pattern and discrete wavelet transform (TP-DWT), including SPCH, gray-level co-occurrence matrix, run length, Shannon entropy, and Pearson correlation. Through proportional factor-based firefly optimization (PFFO), select optimal features to enhance classification accuracy. The selected features are then fed into a multilayer perceptron and deep neural network (MPDNN) for image classification, effectively distinguishing between changed and unchanged image parts. This experiment is conducted in MATLAB, and it demonstrates the superior performance of the proposed PFFO-MPDNN-SAR-IC-CD method across various performance metrics, including accuracy, sensitivity, specificity, rand index, global consistency error, variation of information, false-negative rate (FNR), and false-positive rate (FPR). Comparisons with existing methods, including RBF-DCNN-SAR-IC, DBN-SAR-IC, CNN-SAR-IC, USF-SAR-IC, DDN-SAR-IC, L-CapsNet-SAR-IC, SVM-HRLSM-SAR-IC, and Faster R-CNN-SAR-IC, demonstrate the superiority of this approach in SAR image classification and change detection tasks. This method achieves significant accuracy improvements, particularly in the Yellow River dataset, showcasing its effectiveness and robustness in real-world applications.

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References

  1. Sun, Z., Zhang, Z., Chen, Y., Liu, S., Song, Y.: Frost filtering algorithm of SAR images with adaptive windowing and adaptive tuning factor. IEEE Geosci. Remote Sens. Lett. 17(6), 1097–1101 (2019)

    Article  Google Scholar 

  2. Aghaei, N., Akbarizadeh, G.: Kosarian, :AGreyWolfLSM: an accurate oil spill detection method based on level set method from synthetic aperture radar imagery. Eur. J. Remote Sens. 55(1), 181–198 (2022)

    Article  Google Scholar 

  3. Domínguez, E.M., Meier, E., Small, D., Schaepman, M.E., Bruzzone, L., Henke, D.: A multisquint framework for change detection in high-resolution multitemporal SAR images. IEEE Trans. Geosci. Remote Sens. 56(6), 3611–3623 (2018)

    Article  Google Scholar 

  4. Mahdy, A.M., Higazy, M., Mohamed, M.S.: Optimal and memristor-based control of a nonlinear fractional tumor-immune model. Comput Mater Continua 67(3), 3463–3486 (2021)

    Article  Google Scholar 

  5. Ln, L., Li, J., Yuan, Q., Shen, H.: Polarimetric SAR image super-resolution VIA deep convolutional neural network. In IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 3205-IEEE.M3208) (2019)

  6. Huang, X., Zhang, B., Perrie, W., Lu, Y., Wang, C.: A novel deep learning method for marine oil spill detection from satellite synthetic aperture radar imagery. Mar. Pollut. Bull. 179, 113666 (2022)

    Article  Google Scholar 

  7. Li, H.C., Yang, G., Yang, W., Du, Q., Emery, W.J.: Deep nonsmooth nonnegative matrix factorization network with semi-supervised learning for SAR image change detection. ISPRS J. Photogramm. Remote Sens. 160, 167–179 (2020)

    Article  Google Scholar 

  8. Chen, H., Jiao, L., Liang, M., Liu, F., Yang, S., Hou, B.: Fast unsupervised deep fusion network for change detection of multitemporal SAR images. Neurocomputing 332, 56–70 (2019)

    Article  Google Scholar 

  9. Li, M., Li, M., Zhang, P., Wu, Y., Song, W., An, L.: SAR image change detection using PCANet guided by saliency detection. IEEE Geosci. Remote Sens. Lett. 16(3), 402–406 (2018)

    Article  Google Scholar 

  10. Yang, M., Jiao, L., Liu, F., Hou, B., Yang, S., Jian, M.: DPFL-Nets: deep pyramid feature learning networks for multiscale change detection. IEEE Trans. Neural Netw. Learn. Syst. 33(11), 6402–6416 (2021)

    Article  Google Scholar 

  11. Hosseiny, B., Mahdianpari, M., Hemati, M., Radman, A., Mohammadimanesh, F., Chanussot, J.: Beyond supervised learning in remote sensing: a systematic review of deep learning approaches. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 17, 1035 (2023)

    Article  Google Scholar 

  12. West, R., Yocky, D., Vander Laan, J., Anderson, D., Redman, B.: Data fusion of very high resolution hyperspectral and polarimetric SAR imagery for terrain classification (No. SAND2021–7242). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States). (2021)

  13. Shen, F., Wang, Y., Liu, C.: Synthetic aperture radar image change detection based on Kalman filter and nonlocal means filter in the nonsubsampledshearlet transform domain. J. Appl. Remote. Sens. 14(1), 016517–016517 (2020)

    Article  Google Scholar 

  14. Tian, D., Gong, M.: A novel edge-weight based fuzzy clustering method for change detection in SAR images. Inf. Sci. 467, 415–430 (2018)

    Article  MathSciNet  Google Scholar 

  15. Wang, W., Zhang, C., Tian, J., Ou, J., Li, J.: A SAR image target recognition approach via novel SSF-Net models. Comput. Intell. Neurosci. 2020, 1 (2020)

    Google Scholar 

  16. Gao, F., Huang, T., Sun, J., Wang, J., Hussain, A., Yang, E.: A new algorithm for SAR image target recognition based on an improved deep convolutional neural network. Cogn. Comput. 11, 809–824 (2019)

    Article  Google Scholar 

  17. Shajin, F.H., Rajesh, P.S., Nagoji Rao, V.K.: Efficient framework for brain tumour classification using hierarchical deep learning neural network classifier. Comput. Methods Biomech. Biomed. Eng. Imag. Visual. 11(3), 750–757 (2023)

    Article  Google Scholar 

  18. Ye, F., Luo, W., Dong, M., He, H., Min, W.: SAR image retrieval based on unsupervised domain adaptation and clustering. IEEE Geosci. Remote Sens. Lett. 16(9), 1482–1486 (2019)

    Article  Google Scholar 

  19. Lou, X., Jia, Z., Yang, J., Kasabov, N.: Change detection in SAR images based on the ROF model semi-implicit denoising method. Sensors 19(5), 1179 (2019)

    Article  Google Scholar 

  20. Singh, P., Shree, R.: A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion. J. King Saud Univ. Comput. Info. Sci. 32(1), 137–148 (2020)

    Google Scholar 

  21. Zhu, X.X., Montazeri, S., Ali, M., Hua, Y., Wang, Y., Mou, L., Shi, Y., Xu, F., Bamler, R.: Deep learning meets SAR: concepts, models, pitfalls, and perspectives. IEEE Geosci. Remote Sens. Mag. 9(4), 143–172 (2021)

    Article  Google Scholar 

  22. Datcu, M., Huang, Z., Anghel, A., Zhao, J., Cacoveanu, R.: Explainable, physics-aware, trustworthy artificial intelligence: a paradigm shift for synthetic aperture radar. IEEE Geosci. Remote Sens. Mag. 11(1), 8–25 (2023)

    Article  Google Scholar 

  23. Hosseiny, B., Mahdianpari, M., Brisco, B., Mohammadimanesh, F., Salehi, B.: WetNet: A spatial–temporal ensemble deep learning model for wetland classification using Sentinel-1 and Sentinel-2. IEEE Trans. Geosci. Remote Sens. 60, 1–14 (2021)

    Article  Google Scholar 

  24. Wang, J., Yang, X., Yang, X., Jia, L., Fang, S.: Unsupervised change detection between SAR images based on hypergraphs. ISPRS J. Photogramm. Remote Sens. 164, 61–72 (2020)

    Article  Google Scholar 

  25. El-Sapa, S., Gepreel, K.A., Lotfy, K., El-Bary, A., Mahdy, A.M.S.: Impact of variable thermal conductivity of thermal-plasma-mechanical waves on rotational microelongated excited semiconductor. J. Low Temp. Phys. 209(1–2), 144–165 (2022)

    Article  Google Scholar 

  26. Xuan, P., Sheng, N., Zhang, T., Liu, Y., Guo, Y.: CNNDLP: a method based on convolutional autoencoder and convolutional neural network with adjacent edge attention for predicting lncRNA–disease associations. Int. J. Mol. Sci. 20(17), 4260 (2019)

    Article  Google Scholar 

  27. Karimi, D., Akbarizadeh, G., Rangzan, K., Kabolizadeh, M.: Effective supervised multiple-feature learning for fused radar and optical data classification. IET Radar Sonar Navig. 11(5), 768–777 (2017)

    Article  Google Scholar 

  28. Karimi, D., Rangzan, K., Akbarizadeh, G., Kabolizadeh, M.: Combined algorithm for improvement of fused radar and optical data classification accuracy. J. Electron. Imaging 26(1), 013017–013017 (2017)

    Article  Google Scholar 

  29. https://www.kaggle.com/berntao

  30. https://www.kaggle.com/datasets/tejusrevi/ottawa-real-estate-data

  31. https://www.kaggle.com/questions-and-answers/101267

  32. https://www.kaggle.com/code/vbmokin/datasets-for-river-water-quality-prediction

  33. Geng, J., Jiang, W., Deng, X.: Multi-scale deep feature learning network with bilateral filtering for SAR image classification. ISPRS J. Photogramm. Remote Sens. 167, 201–213 (2020)

    Article  Google Scholar 

  34. Mondal, K., Rabidas, R., Dasgupta, R.: Single image haze removal using contrast limited adaptive histogram equalization based multiscale fusion technique. Multim. Tools Appl. 83(5), 15413–15438 (2024)

    Article  Google Scholar 

  35. Ghosh, C., Majumdar, D., Mondal, B.: SAR Image change detection using modified gauss-log ratio operator and convolution neural network. In Proceedings of Research and Applications in Artificial Intelligence: RAAI 2020 (pp. 223–232). Springer Singapore. (2021)

  36. Tuncer, T., Dogan, S., Subasi, A.: Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition. Biomed. Signal Process. Control 58, 101872 (2020)

    Article  Google Scholar 

  37. Kumar, R., Talukdar, F.A., Dey, N., Balas, V.E.: Quality factor optimisation of spiral inductor using firefly algorithm and its application in amplifier. Int. J. Adv. Intell. Paradig. 11(3–4), 299–314 (2018)

    Google Scholar 

  38. Masih, N., Naz, H., Ahuja, S.: Multilayer perceptron based deep neural network for early detection of coronary heart disease. Heal. Technol. 11, 127–138 (2021)

    Article  Google Scholar 

Download references

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Engineering, Government Polytechnic College for Women, Madurai, Tamil Nadu, India

    B. Pandeeswari

  2. Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India

    K. Alice

  3. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India

    J. Sutha

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  1. B. Pandeeswari
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  2. K. Alice
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  3. J. Sutha
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Contributions

Dr. B. Pandeeswari (corresponding author) contributed to conceptualization, methodology, and writing—original draft preparation. Dr. K. Alice was involved in supervision. Dr. J. Sutha contributed to supervision.

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Correspondence to B. Pandeeswari.

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Pandeeswari, B., Alice, K. & Sutha, J. Optimizing synthetic aperture radar image classification and change detection: a proportional factor firefly algorithm and multilayer perceptron approach. SIViP (2024). https://doi.org/10.1007/s11760-024-03191-4

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  • DOI: https://doi.org/10.1007/s11760-024-03191-4

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