High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN
To overcome the problems in existing infrared remote sensing image generation methods, which make it difficult to combine high fidelity and high efficiency, we propose a High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixG...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4350 |
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| _version_ | 1850107295665487872 |
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| author | Yue Li Xiaorui Wang Chao Zhang Zhonggen Zhang Fafa Ren |
| author_facet | Yue Li Xiaorui Wang Chao Zhang Zhonggen Zhang Fafa Ren |
| author_sort | Yue Li |
| collection | DOAJ |
| description | To overcome the problems in existing infrared remote sensing image generation methods, which make it difficult to combine high fidelity and high efficiency, we propose a High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN (HFIRSIGM_GRSMP) in this paper. Firstly, based on the global radiation scattering mechanism, the HFIRSIGM_GRSMP model is constructed to address the problem of accurately characterizing factors that affect fidelity—such as the random distribution of the radiation field, multipath scattering, and nonlinear changes—through the innovative fusion of physical models and deep learning. This model accurately characterizes the complex radiation field distribution and the image detail-feature mapping relationship from visible-to-infrared remote sensing. Then, 8000 pairs of image datasets were constructed based on Landsat 8 and Sentinel-2 satellite data. Finally, the experiment demonstrates that the average SSIM of images generated using HFIRSIGM_GRSMP reaches 89.16%, and all evaluation metrics show significant improvement compared to the contrast models. More importantly, this method demonstrates high accuracy and strong adaptability in generating short-wave, mid-wave, and long-wave infrared remote sensing images. This method provides a more comprehensive solution for generating high-fidelity infrared remote sensing images. |
| format | Article |
| id | doaj-art-4d0931460e1b4c729cb9e9424ccfecb5 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-4d0931460e1b4c729cb9e9424ccfecb52025-08-20T02:38:36ZengMDPI AGRemote Sensing2072-42922024-11-011623435010.3390/rs16234350High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGANYue Li0Xiaorui Wang1Chao Zhang2Zhonggen Zhang3Fafa Ren4School of Optoelectronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Optoelectronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Optoelectronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Optoelectronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Optoelectronic Engineering, Xidian University, Xi’an 710071, China To overcome the problems in existing infrared remote sensing image generation methods, which make it difficult to combine high fidelity and high efficiency, we propose a High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN (HFIRSIGM_GRSMP) in this paper. Firstly, based on the global radiation scattering mechanism, the HFIRSIGM_GRSMP model is constructed to address the problem of accurately characterizing factors that affect fidelity—such as the random distribution of the radiation field, multipath scattering, and nonlinear changes—through the innovative fusion of physical models and deep learning. This model accurately characterizes the complex radiation field distribution and the image detail-feature mapping relationship from visible-to-infrared remote sensing. Then, 8000 pairs of image datasets were constructed based on Landsat 8 and Sentinel-2 satellite data. Finally, the experiment demonstrates that the average SSIM of images generated using HFIRSIGM_GRSMP reaches 89.16%, and all evaluation metrics show significant improvement compared to the contrast models. More importantly, this method demonstrates high accuracy and strong adaptability in generating short-wave, mid-wave, and long-wave infrared remote sensing images. This method provides a more comprehensive solution for generating high-fidelity infrared remote sensing images. https://www.mdpi.com/2072-4292/16/23/4350coupled physical modeling and deep learningglobal radiative scattering mechanismPix2PixGANinfrared remote sensing image generation |
| spellingShingle | Yue Li Xiaorui Wang Chao Zhang Zhonggen Zhang Fafa Ren High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN Remote Sensing coupled physical modeling and deep learning global radiative scattering mechanism Pix2PixGAN infrared remote sensing image generation |
| title | High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN |
| title_full | High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN |
| title_fullStr | High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN |
| title_full_unstemmed | High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN |
| title_short | High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN |
| title_sort | high fidelity infrared remote sensing image generation method coupled with the global radiation scattering mechanism and pix2pixgan |
| topic | coupled physical modeling and deep learning global radiative scattering mechanism Pix2PixGAN infrared remote sensing image generation |
| url | https://www.mdpi.com/2072-4292/16/23/4350 |
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