English(EN) Learning the Target Priors Before Image Translation: A Decoupled Training Paradigm for Cross-Modal Image Translation in Remote Sensing

新的训练范式改进遥感图像翻译

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员开发了一种名为“学习目标先验再进行图像翻译”（LTP-BIT）的新训练范式，用于遥感中的跨模态图像翻译。该方法将目标域生成先验的学习与跨模态依赖性解耦，解决了配对数据稀缺的问题。LTP-BIT首先从非配对图像中学习先验，然后使用参数高效的双流架构进行源条件控制。实验表明，该方法在SAR到RGB和NIR到RGB基准测试中取得了最先进的性能，显著提高了目标域的真实感和实例保真度。 AI

影响这种新的训练范式可以提高遥感图像翻译任务的准确性和效率，可能有利于环境监测和城市规划等应用。

排序理由详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Keyan Hu, Mingtao Wang, Ziyu Zhou, Tiandong Shi, Haifeng Li, Ji Qi, Chao Tao · 2026-08-31 04:00

遥感跨模态图像翻译中的解耦训练范式：图像翻译前学习目标先验

arXiv:2608.28517v1 Announce Type: new Abstract: Cross-modal image translation in remote sensing must preserve source-observed content while matching the target-domain distribution. Existing methods jointly learn the target prior and cross-modal dependence from scarce paired data,…

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遥感跨模态图像翻译中的解耦训练范式：图像翻译前学习目标先验

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