# Seg-CycleGAN : SAR-to-optical image translation guided by a downstream task

Source: [Seg-CycleGAN : SAR-to-optical image translation guided by a downstream task](<https://www.sota2.com/research/paper/68fa74721e209131cea61dd3>)

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

Data updated: 2025-10-27T06:38:02.139Z

Authors: Hannuo Zhang, Huihui Li, Jiarui Lin, Yujie Zhang, Jianghua Fan, Hang Liu

Published: 2024-08-11T14:01:21.000Z

## Abstract

Optical remote sensing and Synthetic Aperture Radar(SAR) remote sensing are crucial for earth observation, offering complementary capabilities. While optical sensors provide high-quality images, they are limited by weather and lighting conditions. In contrast, SAR sensors can operate effectively under adverse conditions. This letter proposes a GAN-based SAR-to-optical image translation method named Seg-CycleGAN, designed to enhance the accuracy of ship target translation by leveraging semantic information from a pre-trained semantic segmentation model. Our method utilizes the downstream task of ship target semantic segmentation to guide the training of image translation network, improving the quality of output Optical-styled images. The potential of foundation-model-annotated datasets in SAR-to-optical translation tasks is revealed. This work suggests broader research and applications for downstream-task-guided frameworks. The code will be available at https://github.com/NPULHH/

## 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/2408.05777>)

[PDF](<https://arxiv.org/pdf/2408.05777.pdf>)