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

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Title: MDD-VIR: Vis-to-IR Remote Sensing Image Generation Method Based on Mechanism-Data Dual-Driven Strategy.
Authors: Li, Yue1 (AUTHOR), Sun, Dechang1 (AUTHOR), Wang, Xiaorui1 (AUTHOR), Ren, Fafa1 (AUTHOR), Zhang, Chao1 (AUTHOR) chaoxdu@xidian.edu.cn
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1502. 26p.
Subjects: Optical remote sensing, Remote-sensing images, Feature extraction, Image quality analysis, Generative artificial intelligence
Abstract: Highlights: What are the main findings? A Vis-to-IR remote sensing image generation method based on mechanism-data dual-driven strategy (MDD-VIR) is proposed, which couples the global radiation scattering mechanism of cross-band remote sensing imagery with a deep generative model for infrared remote sensing images. Diverse experimental results demonstrate that MDD-VIR exhibits outstanding accuracy and effectiveness in complex terrain scenarios and multi-band infrared remote sensing image generation tasks, with the generated images achieving an average structural similarity index measure (SSIM) value of 91.07%. What are the implications of the main findings? This method addresses critical challenges limiting the accuracy and fidelity of traditional simulation models through a synergistic mechanism-data dual-driven design, and achieves multiple objectives encompassing strong physical consistency, high fidelity, and high efficiency. This method synergistically exploits the unique advantages of mechanism-driven and data-driven paradigms, significantly improves the overall performance of generative models and providing an interpretable, more comprehensive solution for remote sensing image generation across visible to infrared wavelengths. 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 (LossCO) 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. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: MDD-VIR: Vis-to-IR Remote Sensing Image Generation Method Based on Mechanism-Data Dual-Driven Strategy.
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  Data: Highlights: What are the main findings? A Vis-to-IR remote sensing image generation method based on mechanism-data dual-driven strategy (MDD-VIR) is proposed, which couples the global radiation scattering mechanism of cross-band remote sensing imagery with a deep generative model for infrared remote sensing images. Diverse experimental results demonstrate that MDD-VIR exhibits outstanding accuracy and effectiveness in complex terrain scenarios and multi-band infrared remote sensing image generation tasks, with the generated images achieving an average structural similarity index measure (SSIM) value of 91.07%. What are the implications of the main findings? This method addresses critical challenges limiting the accuracy and fidelity of traditional simulation models through a synergistic mechanism-data dual-driven design, and achieves multiple objectives encompassing strong physical consistency, high fidelity, and high efficiency. This method synergistically exploits the unique advantages of mechanism-driven and data-driven paradigms, significantly improves the overall performance of generative models and providing an interpretable, more comprehensive solution for remote sensing image generation across visible to infrared wavelengths. 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 (LossCO) 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Feature extraction
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      – SubjectFull: Image quality analysis
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      – SubjectFull: Generative artificial intelligence
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      – TitleFull: MDD-VIR: Vis-to-IR Remote Sensing Image Generation Method Based on Mechanism-Data Dual-Driven Strategy.
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              Text: May2026
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