Two-Stage Pansharpening Based on Multi-Level Detail Injection Network
Pansharpening is an effective technology to obtain high resolution multispectral (HRMS) images by fusing low resolution multispectral (LRMS) images and high resolution panchromatic (PAN) images. With the rapid development of deep learning, some pansharpening methods based on deep learning have been...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9174972/ |
| _version_ | 1818444587401740288 |
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| author | Jianwen Hu Chenguang Du Shaosheng Fan |
| author_facet | Jianwen Hu Chenguang Du Shaosheng Fan |
| author_sort | Jianwen Hu |
| collection | DOAJ |
| description | Pansharpening is an effective technology to obtain high resolution multispectral (HRMS) images by fusing low resolution multispectral (LRMS) images and high resolution panchromatic (PAN) images. With the rapid development of deep learning, some pansharpening methods based on deep learning have been proposed. Although fused images are greatly improved, there are still some areas for improvement. For example, the spectral preservation is not good enough and the details of fused images are not rich enough. To address the above problems, a two-stage pansharpening method based on convolutional neural network (CNN) is proposed. In the first stage, image super-resolution technology with residual block is used to enhance LRMS. In order to preserve spectra, inspired by the SAM (spectral angle mapper) index, a new spectral loss function is proposed. The second stage is the fusion stage. Detail injection block is proposed by combining detail injection and CNN in this stage. Experiments on WorldView2 and GeoEye1 images demonstrate that our fused images present more spatial details and better spectra by comparing with existing methods. |
| first_indexed | 2024-12-14T19:18:18Z |
| format | Article |
| id | doaj.art-11419f4f773146afb3d521cc05bdf13d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:18:18Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-11419f4f773146afb3d521cc05bdf13d2022-12-21T22:50:26ZengIEEEIEEE Access2169-35362020-01-01815644215645510.1109/ACCESS.2020.30192019174972Two-Stage Pansharpening Based on Multi-Level Detail Injection NetworkJianwen Hu0https://orcid.org/0000-0001-9849-1327Chenguang Du1Shaosheng Fan2School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, ChinaPansharpening is an effective technology to obtain high resolution multispectral (HRMS) images by fusing low resolution multispectral (LRMS) images and high resolution panchromatic (PAN) images. With the rapid development of deep learning, some pansharpening methods based on deep learning have been proposed. Although fused images are greatly improved, there are still some areas for improvement. For example, the spectral preservation is not good enough and the details of fused images are not rich enough. To address the above problems, a two-stage pansharpening method based on convolutional neural network (CNN) is proposed. In the first stage, image super-resolution technology with residual block is used to enhance LRMS. In order to preserve spectra, inspired by the SAM (spectral angle mapper) index, a new spectral loss function is proposed. The second stage is the fusion stage. Detail injection block is proposed by combining detail injection and CNN in this stage. Experiments on WorldView2 and GeoEye1 images demonstrate that our fused images present more spatial details and better spectra by comparing with existing methods.https://ieeexplore.ieee.org/document/9174972/Pansharpeningdetail injection blockresidual learningconvolutional neural network |
| spellingShingle | Jianwen Hu Chenguang Du Shaosheng Fan Two-Stage Pansharpening Based on Multi-Level Detail Injection Network IEEE Access Pansharpening detail injection block residual learning convolutional neural network |
| title | Two-Stage Pansharpening Based on Multi-Level Detail Injection Network |
| title_full | Two-Stage Pansharpening Based on Multi-Level Detail Injection Network |
| title_fullStr | Two-Stage Pansharpening Based on Multi-Level Detail Injection Network |
| title_full_unstemmed | Two-Stage Pansharpening Based on Multi-Level Detail Injection Network |
| title_short | Two-Stage Pansharpening Based on Multi-Level Detail Injection Network |
| title_sort | two stage pansharpening based on multi level detail injection network |
| topic | Pansharpening detail injection block residual learning convolutional neural network |
| url | https://ieeexplore.ieee.org/document/9174972/ |
| work_keys_str_mv | AT jianwenhu twostagepansharpeningbasedonmultileveldetailinjectionnetwork AT chenguangdu twostagepansharpeningbasedonmultileveldetailinjectionnetwork AT shaoshengfan twostagepansharpeningbasedonmultileveldetailinjectionnetwork |