用模式归一化加速雷达图像分割，提升收敛速度与稳定性。
U-NetMN and SegNetMN: Modified U-Net and SegNet models for bimodal SAR image segmentation
- 在U-Net和SegNet中引入模式归一化，改善数据分布复杂性带来的训练问题。
- 实验显示归一化模型收敛速度显著加快，且跨区域验证稳定性提升。
- 适合需要高效、稳定分割SAR图像的遥感应用开发者使用。
合成孔径雷达（SAR）图像分割对遥感应用（尤其是水体检测）至关重要。然而，深度学习分割模型常因数据复杂的统计分布而面临收敛慢、不稳定的挑战。本研究评估了模式归一化对两种主流语义分割模型——U-Net和SegNet的影响。通过引入模式归一化，有效缩短了收敛时间，同时保持基线模型性能。实验结果表明，归一化显著加快收敛；交叉验证显示，归一化模型在不同区域间表现出更强的稳定性。这些发现证实了归一化在提升SAR图像分割计算效率与泛化能力方面的有效性。
原文摘要 · Abstract (English)
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.
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