# U-NetMN and SegNetMN: Modified U-Net and SegNet models for bimodal SAR image segmentation

Source: [U-NetMN and SegNetMN: Modified U-Net and SegNet models for bimodal SAR image segmentation](<https://www.sota2.com/research/paper/68fa792b1e209131cea7c5f3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-17T05:02:44.796Z

Authors: Marwane Kzadri, Franco Alberto Cardillo, Nan\\'ee Chahinian, Carole Delenne, Renaud Hostache, Jamal Riffi

Published: 2025-06-05T13:32:38.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2506.05444>)

[PDF](<https://arxiv.org/pdf/2506.05444.pdf>)