MDD-VIR: Vis-to-IR Remote Sensing Image Generation Method Based on Mechanism-Data Dual-Driven Strategy

High-fidelity infrared remote sensing imagery serves as a critical foundation for the development of technologies such as infrared scene simulation and long-range imaging detection. Addressing the core limitations of two categories of methods: traditional physical modeling methods—low fidelity and efficiency—and deep learning-based generation methods with insufficient interpretability and weak generalization capabilities, we propose a visible-to-infrared (Vis-to-IR) remote sensing image generation method based on the multi-dimensional features of scene elements and mechanism-data dual-driven strategy (MDD-VIR) in this paper. First, a scene element multi-dimensional feature extractor (SEMFE) is designed by analyzing and reconstructing limited datasets, bridging physical mechanisms and intelligent learning. From a game-theoretic perspective, we present a Unet3+-based frequency-domain adaptive spatial channel reconstruction convolution module (FASCRC_Unet3+) and a feature fusion discrimination method based on proactive material weighting (FFD_PMW) to enhance the model’s ability to learn and transform high-value regional and multi-scale features. Furthermore, a collaborative optimization loss function (Loss_CO) is designed to integrate dual-driven paradigm advantages to facilitate efficient iteration. Experiments show that the average SSIM of MDD-VIR simulated images reached 91.07%. Innovatively fusing physical algorithms with intelligent models, this approach enables the Vis-to-IR remote sensing image generation model to achieve the multiple objectives of robust physical consistency, high fidelity, and high efficiency.