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

2026-08-28

AI Summary

本文针对遥感中的跨模态图像转换问题，提出LTP-BIT方法，先学习目标域生成先验，再通过P-DART架构学习源条件控制，提高了目标域真实性和实例保真度。

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, overlooking a key asymmetry: only the latter intrinsically requires cross-modal correspondence. We formalize this distinction through conditional-score and denoising-risk analyses and propose Learning the Target Priors Before Image Translation (LTP-BIT), a prior-first paradigm that decouples the two learning tasks. LTP-BIT first learns a target-domain generative prior from large-scale unpaired imagery, then retains the pretrained backbone weights and learns source-conditioned control through P-DART, a parameter-efficient dual-stream architecture. Controlled experiments show that prior matching and scaling primarily improve target-domain realism, whereas instance fidelity relies more strongly on conditional adaptation. LTP-BIT achieves state-of-the-art performance across SAR-to-RGB and NIR-to-RGB benchmarks using only 9.81% task-specific parameters. On QXS-SAROPT, it retains near-full-data instance fidelity with only 25% of the paired samples.

Problem

• Cross-modal Image Translation
• Remote Sensing
• Target Priors
• Cross-modal Dependence
• Paired Data

Innovation

• Cross-modal Image Translation
• Target Prior Learning
• Decoupled Training Paradigm
• P-DART Architecture
• Parameter Efficiency