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Research Article |

Adaptive Fuzzy Network Denoising for Enhanced Thin Ice Visualization in Cross-Polarized Sentinel-1 SAR

Author(s): Halit NAZLI1, Osman YILDIRIM2*

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 14, Issue 1

Publisher : FOREX Publication

Published : 10 March 2026

e-ISSN : 2347-470X

Page(s) : 38-55

DOI: https://doi.org/10.37391/IJEER.140105

Abstract

Detecting thin (baby) ice in HV-polarized Sentinel-1 extra-wide (EW) sea-ice SAR images is challenging because thermal noise and the scalloping effect can mask weak backscatter signals. This paper proposes an adaptive denoising and thresholding approach using a self-designed fuzzy logic controller network (FLCN) to enhance baby-ice visualization. The approach automatically selects “no object” and “minimum object” regions and applies a data-driven correction factor to improve noise suppression without relying on external parameters. The FLCN generates input–output membership functions autonomously, reducing the need for manual tuning. We compare the results against the ESA/SNAP noise-vector correction workflow, a recent Q1 EW sea-ice denoising method [5], and a deep-learning denoiser (DnCNN). A quantitative and qualitative evaluation over multiple Sentinel-1 EW HV scenes using ROI-based open-water statistics (mean and standard deviation) and an SNR measure shows consistent noise suppression while preserving the ice–water boundaries compared to other recent methods.

Keywords: Thermal noise, Scalloping effect SAR, Fuzzy logic network, SAR, Speckles noise.




Halit NAZLI, Electrical Electronic Engineering, Istanbul Aydin University, Istanbul, Turkey;Email: khaledalnazli@stu.aydin.edu.tr

Osman YILDIRIM ,Electrical Electronic Engineering, Istanbul Aydin University, Istanbul, Turkey; Email: osmanyildirim@aydin.edu.tr

    [1] N. Ivanova, O. M. Johannessen, L. T. Pedersen and R. T. Tonboe, "Retrieval of Arctic Sea Ice Parameters by Satellite Passive Microwave Sensors: A Comparison of Eleven Sea Ice Concentration Algorithms," in IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 11, pp. 7233-7246, Nov. 2014, doi: 10.1109/TGRS.2014.2310136.
    [2] L. Hermozo, L. Eymard and F. Karbou, "Modeling Sea Ice Surface Emissivity at Microwave Frequencies: Impact of the Surface Assumptions and Potential Use for Sea Ice Extent and Type Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 2, pp. 943-961, Feb. 2017, doi: 10.1109/TGRS.2016.2616920.
    [3] C. Li, S. Park, D. -J. Kim and J. Kim, "A Deep Learning Based Self-Evolving Oil Spill Detection Algorithm Using Sentinel-1 SAR Images," IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 1289-1292, doi: 10.1109/IGARSS52108.2023.10281695.
    [4] J. Liu, L. Liu and J. Xiao, "Ellipse Polar Encoding for Oriented SAR Ship Detection," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 3502-3515, 2024, doi: 10.1109/JSTARS.2024.3352098.
    [5] Y. Sun and X. M. Li, "Denoising Sentinel-1 Extra Wide Mode Cross-Polarization Image Over Sea Ice," IEEE Trans. Geosci. Remote Sens., vol. 59, no. 3, pp. 2116-2130, 2021.
    [6] Y. Gao, C. Guan, J. Sun, and L. Xie, “A wind speed retrieval model for sentinel-1A EW mode cross-polarization images,” Remote Sens., vol. 11, no. 2, p. 153, Jan. 2019.
    [7] L. Liu, Z. Jia, J. Yang and N. K. Kasabov, "SAR Image Change Detection Based on Mathematical Morphology and the K-Means Clustering Algorithm," in IEEE Access, vol. 7, pp. 43970-43978, 2019, doi: 10.1109/ACCESS.2019.2908282.
    [8] J. Karvonen, “Baltic Sea ice concentration estimation using Sentinel-1 SAR and AMSR2 microwave radiometer data,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 5, pp. 2871–2883, May 2017.
    [9] M. Iqbal, J. Chen, W. Yang, P. Wang, and B. Sun, “Kalman filter for removal of scalloping and inter-scan banding in scansar images,” Prog.Electromagn. Res., vol. 132, pp. 443–461, 2012.
    [10] R. Romeiser, J. Horstmann, M. J. Caruso, and H. C. Graber, “A descalloping postprocessor for ScanSAR images of ocean scenes,” IEEE Trans.Geosci. Remote Sens., vol. 51, no. 6, pp. 3259–3272, Jun. 2013.
    [11] J. -W. Park, A. A. Korosov, M. Babiker, S. Sandven and J. -S. Won, "Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel," in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 3, pp. 1555-1565, March 2018, doi: 10.1109/TGRS.2017.2765248.
    [12] J.-W. Park, A. A. Korosov, M. Babiker, S. Sandven, and J.-S. Won,“Efficient thermal noise removal for Sentinel-1 TOPSAR crosspolarization channel,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 3, pp. 1555–1565, Mar. 2018.
    [13] J.-W. Park, J.-S. Won, A. A. Korosov, M. Babiker, and N. Miranda, “Textural noise correction for Sentinel-1 TOPSAR cross-polarization channel images,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 6, pp. 4040–4049, Jun. 2019.
    [14] J. Li, L. Lin, M. He, J. He, Q. Yuan and H. Shen, "Sentinel-1 Dual-Polarization SAR Images Despeckling Network Based on Unsupervised Learning," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-15, 2024, Art no. 5106315, doi: 10.1109/TGRS.2024.3404405.
    [15] I. -C. Sang and W. R. Norris, "An Adaptive Image Thresholding Algorithm Using Fuzzy Logic for Autonomous Underwater Vehicle Navigation," in IEEE Journal of Selected Topics in Signal Processing, vol. 18, no. 3, pp. 358-367, April 2024, doi: 10.1109/JSTSP.2024.3426484.
    [16] X. Yan, "Application of Image Segmenting Technology Based on Fuzzy C-Means Algorithm in Competition Video Referee," in IEEE Access, vol. 12, pp. 34378-34389, 2024, doi: 10.1109/ACCESS.2024.3355465.
    [17] L. Reznik, Fuzzy Controllers Handbook: How to Design Them, How They Work. Elsevier, 1997.
    [18] A. K. Pandey, H. R. S. S. N. Chatla, M. Pandya, A. Farhan M A and A. S. Rana, "Image Edge Detection Using Fuzzy Logic Controller," 2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON), New Delhi, India, 2023, pp. 508-513, doi: 10.1109/REEDCON57544.2023.
    [19] S. Singha, T. J. Bellerby and O. Trieschmann, "Satellite Oil Spill Detection Using Artificial Neural Networks," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 6, pp. 2355-2363, Dec. 2013, doi: 10.1109/JSTARS.2013.2251864.
    [20] M. Chowdhury, Junbin Gao and R. Islam, "Fuzzy logic-based filtering for image de-noising," 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, BC, Canada, 2016, pp. 2372-2376, doi: 10.1109/FUZZ-IEEE.2016.7737990.
    [21] P. Q. Lee, L. Xu and D. A. Clausi, "Estimating Noise Floor in Sentinel-1 Images with Linear Programming and Least Squares," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14, 2022, Art no. 5212414, doi: 10.1109/TGRS.2021.3101455.
    [22] G. Zhou et al., "HPHR-SAR-Net: Hyperpixel High-Resolution SAR Imaging Network Based on Nonlocal Total Variation," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 8595-8608, 2023, doi: 10.1109/JSTARS.2023.3295728.
    [23] R. Shang et al., "SAR Image Segmentation Based on Constrained Smoothing and Hierarchical Label Correction," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022, Art no. 5102216, doi: 10.1109/TGRS.2021.3076446.
    [24] F. Gu, H. Zhang and C. Wang, "A Two-Component Deep Learning Network for SAR Image Denoising," in IEEE Access, vol. 8, pp. 17792-17803, 2020, doi: 10.1109/ACCESS.2020.2965173.
    [25] S. Liu, S. Tian, Y. Zhao, Q. Hu, B. Li and Y. -D. Zhang, "LG-DBNet: Local and Global Dual-Branch Network for SAR Image Denoising," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-15, 2024, Art no. 5205515, doi: 10.1109/TGRS.2024.3362510.
    [26] P. T. Hien and I. -P. Hong, "Millimeter Wave SAR Imaging Denoising and Classification by Combining Image-to-Image Translation with ResNet," in IEEE Access, vol. 11, pp. 70203-70215, 2023, doi: 10.1109/ACCESS.2023.3293644.
    [27] L. Wang, Z. Niu, R. Shang, Y. Qin, L. Wang, and H. Chen, "High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2, and Landsat-8 data," Int. J. Appl. Earth Obs. Geoinf., vol. 92, p. 102163, 2020. [Online]. Available: https://doi.org/10.1016/j.jag.2020.102163.
    [28] E. Alhatami, B. Uzair Aslam, M. Huang and S. Feng, "Advanced Fuzzy Denoising Technique for Agricultural Remote Sensing: Modified Partition Filter for Suppressing Impulsive," in IEEE Access, vol. 12, pp. 159025-159035, 2024, doi: 10.1109/ACCESS.2024.3447704
    [29] S. Shi, S. Yan, Y. Wang and Y. Li, "Medical Ultrasound Image Denoising Based on Fuzzy Logic," 2014 Fifth International Conference on Intelligent Systems Design and Engineering Applications, Hunan, China, 2014, pp. 611-614, doi: 10.1109/ISDEA.2014.143.
    [30] S. Dhar and M. K. Kundu, "Interval Type-2 Fuzzy Set and Theory of Weak Continuity Constraints for Accurate Multiclass Image Segmentation," in IEEE Transactions on Fuzzy Systems, vol. 28, no. 9, pp. 2151-2163, Sept. 2020, doi: 10.1109/TFUZZ.2019.2930932.
    [31] C. Guan, S. Wang and A. W. -C. Liew, "Lip Image Segmentation Based on a Fuzzy Convolutional Neural Network," in IEEE Transactions on Fuzzy Systems, vol. 28, no. 7, pp. 1242-1251, July 2020, doi: 10.1109/TFUZZ.2019.2957708.
    [32] U. Javed, M. M. Riaz, A. Ghafoor and T. A. Cheema, "SAR image segmentation based on active contours with fuzzy logic," in IEEE Transactions on Aerospace and Electronic Systems, vol. 52, no. 1, pp. 181-188, February 2016, doi: 10.1109/TAES.2015
    [33] Subhedar, M., & Birajdar, G. (2013). Comparison of mamdani and sugeno inference systems for dynamic spectrum allocation in cognitive radio networks. Wireless personal communications, 71, 805-819.
    [34] SNAP - ESA Sentinel Application Platform v{9}, http://step.esa.int (access on 14/02/2024).

Halit NAZLI, and Osman YILDIRIM(2026),Adaptive Fuzzy Network Denoising for Enhanced Thin Ice Visualization in Cross-Polarized Sentinel-1 SAR . IJEER 14(1), 38-55. DOI: 10.37391/IJEER.140105.