Multi-spectral remote sensing image fusion method based on gradient moment matching
Image fusion is a popular research direction in the field of computer vision. Traditional image fusion algorithms can achieve good results in fusing grayscale images, but it is difficult to achieve ideal results in processing multi-spectral images. To address the shortcomings of multi-spectral image...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000371 |
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| author | Haiying Fan Gonghuai Wei |
| author_facet | Haiying Fan Gonghuai Wei |
| author_sort | Haiying Fan |
| collection | DOAJ |
| description | Image fusion is a popular research direction in the field of computer vision. Traditional image fusion algorithms can achieve good results in fusing grayscale images, but it is difficult to achieve ideal results in processing multi-spectral images. To address the shortcomings of multi-spectral image fusion, this study proposes a low computational complexity and low latency multi-spectral image fusion model by utilizing a multi-step degree moment matching algorithm and a generative adversarial network for fusion. Through experiments, it was found that the F1 score of the GAN-MMN model on the TinyPerson dataset was 89.79 %, with an average recall rate of 89.76 %. The GAN-MMN performance was higher than that of the control model. Meanwhile, the GAN-MMN model also exhibited superior performance in high-frequency feature extraction and time delay compared to the control model. According to the experimental results, the proposed multi-spectral remote sensing image fusion model had a high feature extraction effect, and its recall rate and F1 score were better than the control model, so the research model had a certain progressiveness. The proposal of this model gives a new approach for the processing of multi-spectral remote sensing images, effectively promoting the development of the computer vision industry. |
| format | Article |
| id | doaj-art-837703d6cb0b4c438432b32488f2f7d2 |
| institution | OA Journals |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-837703d6cb0b4c438432b32488f2f7d22025-08-20T02:34:40ZengElsevierSystems and Soft Computing2772-94192024-12-01620010810.1016/j.sasc.2024.200108Multi-spectral remote sensing image fusion method based on gradient moment matchingHaiying Fan0Gonghuai Wei1School of Resources and Civil Engineering, Liaoning Institute of Science and Technology, Benxi, 117004, ChinaShandong Luqiao Group Co., Ltd, Ji'nan, 250014, China; Corresponding author.Image fusion is a popular research direction in the field of computer vision. Traditional image fusion algorithms can achieve good results in fusing grayscale images, but it is difficult to achieve ideal results in processing multi-spectral images. To address the shortcomings of multi-spectral image fusion, this study proposes a low computational complexity and low latency multi-spectral image fusion model by utilizing a multi-step degree moment matching algorithm and a generative adversarial network for fusion. Through experiments, it was found that the F1 score of the GAN-MMN model on the TinyPerson dataset was 89.79 %, with an average recall rate of 89.76 %. The GAN-MMN performance was higher than that of the control model. Meanwhile, the GAN-MMN model also exhibited superior performance in high-frequency feature extraction and time delay compared to the control model. According to the experimental results, the proposed multi-spectral remote sensing image fusion model had a high feature extraction effect, and its recall rate and F1 score were better than the control model, so the research model had a certain progressiveness. The proposal of this model gives a new approach for the processing of multi-spectral remote sensing images, effectively promoting the development of the computer vision industry.http://www.sciencedirect.com/science/article/pii/S2772941924000371Gradient momentRemote sensingImage fusionGenerating adversarial networksMulti-spectral |
| spellingShingle | Haiying Fan Gonghuai Wei Multi-spectral remote sensing image fusion method based on gradient moment matching Systems and Soft Computing Gradient moment Remote sensing Image fusion Generating adversarial networks Multi-spectral |
| title | Multi-spectral remote sensing image fusion method based on gradient moment matching |
| title_full | Multi-spectral remote sensing image fusion method based on gradient moment matching |
| title_fullStr | Multi-spectral remote sensing image fusion method based on gradient moment matching |
| title_full_unstemmed | Multi-spectral remote sensing image fusion method based on gradient moment matching |
| title_short | Multi-spectral remote sensing image fusion method based on gradient moment matching |
| title_sort | multi spectral remote sensing image fusion method based on gradient moment matching |
| topic | Gradient moment Remote sensing Image fusion Generating adversarial networks Multi-spectral |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000371 |
| work_keys_str_mv | AT haiyingfan multispectralremotesensingimagefusionmethodbasedongradientmomentmatching AT gonghuaiwei multispectralremotesensingimagefusionmethodbasedongradientmomentmatching |