U-NetMN and SegNetMN: Modified U-Net and SegNet models for bimodal SAR image segmentation
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908707786326016 |
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| author | Kzadri, Marwane Cardillo, Franco Alberto Chahinian, Nanée Delenne, Carole Hostache, Renaud Riffi, Jamal |
| author_facet | Kzadri, Marwane Cardillo, Franco Alberto Chahinian, Nanée Delenne, Carole Hostache, Renaud Riffi, Jamal |
| contents | Segmenting Synthetic Aperture Radar (SAR) images is crucial for many remote sensing applications, particularly water body detection. However, deep learning-based segmentation models often face challenges related to convergence speed and stability, mainly due to the complex statistical distribution of this type of data. In this study, we evaluate the impact of mode normalization on two widely used semantic segmentation models, U-Net and SegNet. Specifically, we integrate mode normalization, to reduce convergence time while maintaining the performance of the baseline models. Experimental results demonstrate that mode normalization significantly accelerates convergence. Furthermore, cross-validation results indicate that normalized models exhibit increased stability in different zones. These findings highlight the effectiveness of normalization in improving computational efficiency and generalization in SAR image segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05444 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | U-NetMN and SegNetMN: Modified U-Net and SegNet models for bimodal SAR image segmentation Kzadri, Marwane Cardillo, Franco Alberto Chahinian, Nanée Delenne, Carole Hostache, Renaud Riffi, Jamal Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Segmenting Synthetic Aperture Radar (SAR) images is crucial for many remote sensing applications, particularly water body detection. However, deep learning-based segmentation models often face challenges related to convergence speed and stability, mainly due to the complex statistical distribution of this type of data. In this study, we evaluate the impact of mode normalization on two widely used semantic segmentation models, U-Net and SegNet. Specifically, we integrate mode normalization, to reduce convergence time while maintaining the performance of the baseline models. Experimental results demonstrate that mode normalization significantly accelerates convergence. Furthermore, cross-validation results indicate that normalized models exhibit increased stability in different zones. These findings highlight the effectiveness of normalization in improving computational efficiency and generalization in SAR image segmentation. |
| title | U-NetMN and SegNetMN: Modified U-Net and SegNet models for bimodal SAR image segmentation |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2506.05444 |