U-NetMN and SegNetMN: Modified U-Net and SegNet models for bimodal SAR image segmentation
This work addresses computational efficiency and generalization issues for remote sensing applications like water body detection, but is incremental as it modifies existing models with normalization.
The researchers tackled the problem of slow convergence and instability in deep learning models for SAR image segmentation by integrating mode normalization into U-Net and SegNet, resulting in significantly accelerated convergence and increased stability across different zones.
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.