A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks
Multispectral images contain richer spectral signatures than easily available RGB images, for which they are promising to contribute to information perception. However, the relatively high cost of multispectral sensors and lower spatial resolution limit the widespread application of multispectral da...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/11/1901 |
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| author | Xiaoning Zhang Zhaoyang Peng Yifei Wang Fan Ye Tengying Fu Hu Zhang |
| author_facet | Xiaoning Zhang Zhaoyang Peng Yifei Wang Fan Ye Tengying Fu Hu Zhang |
| author_sort | Xiaoning Zhang |
| collection | DOAJ |
| description | Multispectral images contain richer spectral signatures than easily available RGB images, for which they are promising to contribute to information perception. However, the relatively high cost of multispectral sensors and lower spatial resolution limit the widespread application of multispectral data, and existing reconstruction algorithms suffer from a lack of diverse training datasets and insufficient reconstruction accuracy. In response to these issues, this paper proposes a novel and robust multispectral reconstruction network from low-cost natural color RGB images based on free available satellite images with various land cover types. First, to supplement paired natural color RGB and multispectral images, the Houston hyperspectral dataset was used to train a convolutional neural network Model-TN for generating natural color RGB images from true color images combining CIE standard colorimetric system theory. Then, the EuroSAT multispectral satellite images for eight land cover types were selected to produce natural RGB using Model-TN as training image pairs, which were input into a residual network integrating channel attention mechanisms to train the multispectral images reconstruction model, Model-NM. Finally, the feasibility of the reconstructed multispectral images is verified through image classification and target detection. There is a small mean relative absolute error value of 0.0081 for generating natural color RGB images, which is 0.0397 for reconstructing multispectral images. Compared to RGB images, the accuracies of classification and detection using reconstructed multispectral images have improved by 16.67% and 3.09%, respectively. This study further reveals the potential of multispectral image reconstruction from natural color RGB images and its effectiveness in target detection, which promotes low-cost visual perception of intelligent unmanned systems. |
| format | Article |
| id | doaj-art-58f2585218b94e578ebd2d9696ccd85c |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-58f2585218b94e578ebd2d9696ccd85c2025-08-20T03:11:20ZengMDPI AGRemote Sensing2072-42922025-05-011711190110.3390/rs17111901A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection TasksXiaoning Zhang0Zhaoyang Peng1Yifei Wang2Fan Ye3Tengying Fu4Hu Zhang5School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaFaculty of Geographical, Tianjin Normal University, Tianjin 300387, ChinaMultispectral images contain richer spectral signatures than easily available RGB images, for which they are promising to contribute to information perception. However, the relatively high cost of multispectral sensors and lower spatial resolution limit the widespread application of multispectral data, and existing reconstruction algorithms suffer from a lack of diverse training datasets and insufficient reconstruction accuracy. In response to these issues, this paper proposes a novel and robust multispectral reconstruction network from low-cost natural color RGB images based on free available satellite images with various land cover types. First, to supplement paired natural color RGB and multispectral images, the Houston hyperspectral dataset was used to train a convolutional neural network Model-TN for generating natural color RGB images from true color images combining CIE standard colorimetric system theory. Then, the EuroSAT multispectral satellite images for eight land cover types were selected to produce natural RGB using Model-TN as training image pairs, which were input into a residual network integrating channel attention mechanisms to train the multispectral images reconstruction model, Model-NM. Finally, the feasibility of the reconstructed multispectral images is verified through image classification and target detection. There is a small mean relative absolute error value of 0.0081 for generating natural color RGB images, which is 0.0397 for reconstructing multispectral images. Compared to RGB images, the accuracies of classification and detection using reconstructed multispectral images have improved by 16.67% and 3.09%, respectively. This study further reveals the potential of multispectral image reconstruction from natural color RGB images and its effectiveness in target detection, which promotes low-cost visual perception of intelligent unmanned systems.https://www.mdpi.com/2072-4292/17/11/1901multispectral reconstructionresidual networkchannel attentionimage classificationtarget detection |
| spellingShingle | Xiaoning Zhang Zhaoyang Peng Yifei Wang Fan Ye Tengying Fu Hu Zhang A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks Remote Sensing multispectral reconstruction residual network channel attention image classification target detection |
| title | A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks |
| title_full | A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks |
| title_fullStr | A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks |
| title_full_unstemmed | A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks |
| title_short | A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks |
| title_sort | robust multispectral reconstruction network from rgb images trained by diverse satellite data and application in classification and detection tasks |
| topic | multispectral reconstruction residual network channel attention image classification target detection |
| url | https://www.mdpi.com/2072-4292/17/11/1901 |
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