Pansharpening scheme using spatial detail injection–based convolutional neural networks
Abstract Pansharpening produces a high spatial‐spectral resolution pansharpened image by combining multispectral (MS) and panchromatic (PAN) images. In the traditional multi‐resolution analysis (MRA) method, detailed PAN images are extracted by transformation methods that are injected into MS images...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-07-01
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| Series: | IET Image Processing |
| Online Access: | https://doi.org/10.1049/ipr2.12384 |
| _version_ | 1811337264775561216 |
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| author | Nidhi Saxena Gaurav Saxena Neelu Khare Md Habibur Rahman |
| author_facet | Nidhi Saxena Gaurav Saxena Neelu Khare Md Habibur Rahman |
| author_sort | Nidhi Saxena |
| collection | DOAJ |
| description | Abstract Pansharpening produces a high spatial‐spectral resolution pansharpened image by combining multispectral (MS) and panchromatic (PAN) images. In the traditional multi‐resolution analysis (MRA) method, detailed PAN images are extracted by transformation methods that are injected into MS images. This gives spatial and spectral distortions in the pansharpened image. These distortions can be reduced in the pansharpened image by the correct matching of the PAN detail image component. This correct matching is possible by the convolutional neural network (CNN)–based models. This paper obtains the detailed image component using the CNN models. This CNN model extracts the PAN detail image that is suitable for the MRA‐based pansharpening scheme which significantly reduces the spatial and spectral distortions. It is demonstrated by qualitative and quantitative analysis applied on GeoEye‐1 and IKONOS satellite images and shows the effectiveness of the proposed scheme. |
| first_indexed | 2024-04-13T17:51:21Z |
| format | Article |
| id | doaj.art-3db60aa705434a24b8630e578e15246e |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-13T17:51:21Z |
| publishDate | 2022-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-3db60aa705434a24b8630e578e15246e2022-12-22T02:36:40ZengWileyIET Image Processing1751-96591751-96672022-07-011692297230710.1049/ipr2.12384Pansharpening scheme using spatial detail injection–based convolutional neural networksNidhi Saxena0Gaurav Saxena1Neelu Khare2Md Habibur Rahman3Department of Information Technology Madhav Institute of Technology and Science Gwalior Madhya Pradesh IndiaDepartment of Automobile Engineering Rustamji Institute of Technology, BSF Academy Tekanpur Gwalior Madhya Pradesh IndiaSchool of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu IndiaDepartment of Computer Science and Engineering Islamic University Kushtia BangladeshAbstract Pansharpening produces a high spatial‐spectral resolution pansharpened image by combining multispectral (MS) and panchromatic (PAN) images. In the traditional multi‐resolution analysis (MRA) method, detailed PAN images are extracted by transformation methods that are injected into MS images. This gives spatial and spectral distortions in the pansharpened image. These distortions can be reduced in the pansharpened image by the correct matching of the PAN detail image component. This correct matching is possible by the convolutional neural network (CNN)–based models. This paper obtains the detailed image component using the CNN models. This CNN model extracts the PAN detail image that is suitable for the MRA‐based pansharpening scheme which significantly reduces the spatial and spectral distortions. It is demonstrated by qualitative and quantitative analysis applied on GeoEye‐1 and IKONOS satellite images and shows the effectiveness of the proposed scheme.https://doi.org/10.1049/ipr2.12384 |
| spellingShingle | Nidhi Saxena Gaurav Saxena Neelu Khare Md Habibur Rahman Pansharpening scheme using spatial detail injection–based convolutional neural networks IET Image Processing |
| title | Pansharpening scheme using spatial detail injection–based convolutional neural networks |
| title_full | Pansharpening scheme using spatial detail injection–based convolutional neural networks |
| title_fullStr | Pansharpening scheme using spatial detail injection–based convolutional neural networks |
| title_full_unstemmed | Pansharpening scheme using spatial detail injection–based convolutional neural networks |
| title_short | Pansharpening scheme using spatial detail injection–based convolutional neural networks |
| title_sort | pansharpening scheme using spatial detail injection based convolutional neural networks |
| url | https://doi.org/10.1049/ipr2.12384 |
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