SFMHANet: Surface Fitting Constrained Multidimensional Hybrid Attention Network for Aero-Optics Thermal Radiation Effect Correction
When an aircraft is flying at hypervelocity in the atmosphere, the airflow and the optical cowl rub against each other, and the airflow's kinetic energy in the boundary layer is transformed into thermal energy, which causes the cowl's surface temperature to rise nonuniformly an...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10839098/ |
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| author | Yu Shi ShanLin Niu Lei Wang Liang Ye YaoZong Zhang HanYu Hong |
| author_facet | Yu Shi ShanLin Niu Lei Wang Liang Ye YaoZong Zhang HanYu Hong |
| author_sort | Yu Shi |
| collection | DOAJ |
| description | When an aircraft is flying at hypervelocity in the atmosphere, the airflow and the optical cowl rub against each other, and the airflow's kinetic energy in the boundary layer is transformed into thermal energy, which causes the cowl's surface temperature to rise nonuniformly and produces thermal radiation interference with the imaging system of the detector. In practical application scenarios, the aero-optical thermal radiation patterns in degraded images are not fixed, and types of aero-optics thermal radiation are more variable and complex. In order to handle multiple types of aero-optics thermal radiation effects effectively and to combine the advantages of image prior constraints and deep learning networks, we propose a surface fitting constrained multidimensional hybrid attention aero-optics thermal radiation correction network (SFMHANet) in this article. First, according to the characteristics of the aero-optics thermal radiation bias field belonging to low frequency, we initially estimate the aero-optics thermal radiation bias field using biharmonic spline interpolation surface fitting based on wavelet decomposition. Second, we design a multidimensional hybrid attention aero-optics thermal radiation correction network constrained by the supervision of aero-optics thermal radiation bias field for asymmetric information exchange. Finally, to achieve cross-dimensional information interaction of features, we propose a multidimensional hybrid attention module, a second-order pooling channel attention block, and a cross-convolution spatial attention block in the correction network. According to experiments on aero-optics thermal radiation correction of simulated and real degraded images, the SFMHANet can correct the aero-optics thermal radiation effects of multitype degraded images in comparison to other existing methods. |
| format | Article |
| id | doaj-art-f99f18018f9d49118bdb859a709c35c9 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-f99f18018f9d49118bdb859a709c35c92025-02-05T00:00:12ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184569458410.1109/JSTARS.2025.352863010839098SFMHANet: Surface Fitting Constrained Multidimensional Hybrid Attention Network for Aero-Optics Thermal Radiation Effect CorrectionYu Shi0https://orcid.org/0000-0002-8511-2110ShanLin Niu1Lei Wang2https://orcid.org/0000-0002-7383-4167Liang Ye3YaoZong Zhang4https://orcid.org/0000-0002-3727-7957HanYu Hong5https://orcid.org/0000-0003-3544-4472School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, ChinaSchool of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, ChinaSchool of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, ChinaSchool of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, ChinaSchool of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, ChinaSchool of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, ChinaWhen an aircraft is flying at hypervelocity in the atmosphere, the airflow and the optical cowl rub against each other, and the airflow's kinetic energy in the boundary layer is transformed into thermal energy, which causes the cowl's surface temperature to rise nonuniformly and produces thermal radiation interference with the imaging system of the detector. In practical application scenarios, the aero-optical thermal radiation patterns in degraded images are not fixed, and types of aero-optics thermal radiation are more variable and complex. In order to handle multiple types of aero-optics thermal radiation effects effectively and to combine the advantages of image prior constraints and deep learning networks, we propose a surface fitting constrained multidimensional hybrid attention aero-optics thermal radiation correction network (SFMHANet) in this article. First, according to the characteristics of the aero-optics thermal radiation bias field belonging to low frequency, we initially estimate the aero-optics thermal radiation bias field using biharmonic spline interpolation surface fitting based on wavelet decomposition. Second, we design a multidimensional hybrid attention aero-optics thermal radiation correction network constrained by the supervision of aero-optics thermal radiation bias field for asymmetric information exchange. Finally, to achieve cross-dimensional information interaction of features, we propose a multidimensional hybrid attention module, a second-order pooling channel attention block, and a cross-convolution spatial attention block in the correction network. According to experiments on aero-optics thermal radiation correction of simulated and real degraded images, the SFMHANet can correct the aero-optics thermal radiation effects of multitype degraded images in comparison to other existing methods.https://ieeexplore.ieee.org/document/10839098/Aero-optics thermal radiation effect correctionmultidimensional hybrid attention (MHA)surface fittingwavelet decomposition |
| spellingShingle | Yu Shi ShanLin Niu Lei Wang Liang Ye YaoZong Zhang HanYu Hong SFMHANet: Surface Fitting Constrained Multidimensional Hybrid Attention Network for Aero-Optics Thermal Radiation Effect Correction IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Aero-optics thermal radiation effect correction multidimensional hybrid attention (MHA) surface fitting wavelet decomposition |
| title | SFMHANet: Surface Fitting Constrained Multidimensional Hybrid Attention Network for Aero-Optics Thermal Radiation Effect Correction |
| title_full | SFMHANet: Surface Fitting Constrained Multidimensional Hybrid Attention Network for Aero-Optics Thermal Radiation Effect Correction |
| title_fullStr | SFMHANet: Surface Fitting Constrained Multidimensional Hybrid Attention Network for Aero-Optics Thermal Radiation Effect Correction |
| title_full_unstemmed | SFMHANet: Surface Fitting Constrained Multidimensional Hybrid Attention Network for Aero-Optics Thermal Radiation Effect Correction |
| title_short | SFMHANet: Surface Fitting Constrained Multidimensional Hybrid Attention Network for Aero-Optics Thermal Radiation Effect Correction |
| title_sort | sfmhanet surface fitting constrained multidimensional hybrid attention network for aero optics thermal radiation effect correction |
| topic | Aero-optics thermal radiation effect correction multidimensional hybrid attention (MHA) surface fitting wavelet decomposition |
| url | https://ieeexplore.ieee.org/document/10839098/ |
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