Context adaptive panchromatic band simulation and detail injection for image pansharpening
General component substitution (CS) pansharpening methods establish a global model over the whole image plane and may lead to unexpected spectral distortion. This paper proposes a context adaptive CS pansharpening method, which features a totally local-based processing procedure. The method consists...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2018
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| Online Access: | https://hdl.handle.net/10356/86291 http://hdl.handle.net/10220/45240 |
| _version_ | 1826112017310679040 |
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| author | Dai, Qinling Luo, Bin Wang, Leiguang Tu, Zhigang |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Dai, Qinling Luo, Bin Wang, Leiguang Tu, Zhigang |
| author_sort | Dai, Qinling |
| collection | NTU |
| description | General component substitution (CS) pansharpening methods establish a global model over the whole image plane and may lead to unexpected spectral distortion. This paper proposes a context adaptive CS pansharpening method, which features a totally local-based processing procedure. The method consists of two processing blocks. The first block is to simulate a low-resolution panchromatic band by a local linear regression model between panchromatic and multispectral bands. The second block extracts spatial details and adds details back to multispectral bands in locally varying ratios. By recasting the local linear regression model into the guided filtering framework and analyzing the implicit statistical assumptions underlying CS methods, the strengths of the local-based pansharpening algorithm are addressed. Experiments test 7 pairs of images acquired from different sensors, such as GF-2, Quickbird, and Worldview-2. Both quantitative and qualitative evaluations reveal that the presented method can better preserve the spectral information than some state-of-the-art methods. |
| first_indexed | 2024-10-01T03:00:21Z |
| format | Journal Article |
| id | ntu-10356/86291 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:00:21Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/862912020-03-07T13:57:26Z Context adaptive panchromatic band simulation and detail injection for image pansharpening Dai, Qinling Luo, Bin Wang, Leiguang Tu, Zhigang School of Electrical and Electronic Engineering Image Pansharpening Component Substitution General component substitution (CS) pansharpening methods establish a global model over the whole image plane and may lead to unexpected spectral distortion. This paper proposes a context adaptive CS pansharpening method, which features a totally local-based processing procedure. The method consists of two processing blocks. The first block is to simulate a low-resolution panchromatic band by a local linear regression model between panchromatic and multispectral bands. The second block extracts spatial details and adds details back to multispectral bands in locally varying ratios. By recasting the local linear regression model into the guided filtering framework and analyzing the implicit statistical assumptions underlying CS methods, the strengths of the local-based pansharpening algorithm are addressed. Experiments test 7 pairs of images acquired from different sensors, such as GF-2, Quickbird, and Worldview-2. Both quantitative and qualitative evaluations reveal that the presented method can better preserve the spectral information than some state-of-the-art methods. Published version 2018-07-26T01:37:36Z 2019-12-06T16:19:45Z 2018-07-26T01:37:36Z 2019-12-06T16:19:45Z 2018 Journal Article Dai, Q., Luo, B., Wang, L., & Tu, Z. (2018). Context adaptive panchromatic band simulation and detail injection for image pansharpening. Journal of Applied Remote Sensing, 12(1), 015018-. https://hdl.handle.net/10356/86291 http://hdl.handle.net/10220/45240 10.1117/1.JRS.12.015018 en Journal of Applied Remote Sensing © 2018 Society of Photo-Optical Instrumentation Engineers (SPIE). This paper was published in Journal of Applied Remote Sensing and is made available as an electronic reprint (preprint) with permission of Society of Photo-Optical Instrumentation Engineers (SPIE). The published version is available at: [http://dx.doi.org/10.1117/1.JRS.12.015018]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. 19 p. application/pdf |
| spellingShingle | Image Pansharpening Component Substitution Dai, Qinling Luo, Bin Wang, Leiguang Tu, Zhigang Context adaptive panchromatic band simulation and detail injection for image pansharpening |
| title | Context adaptive panchromatic band simulation and detail injection for image pansharpening |
| title_full | Context adaptive panchromatic band simulation and detail injection for image pansharpening |
| title_fullStr | Context adaptive panchromatic band simulation and detail injection for image pansharpening |
| title_full_unstemmed | Context adaptive panchromatic band simulation and detail injection for image pansharpening |
| title_short | Context adaptive panchromatic band simulation and detail injection for image pansharpening |
| title_sort | context adaptive panchromatic band simulation and detail injection for image pansharpening |
| topic | Image Pansharpening Component Substitution |
| url | https://hdl.handle.net/10356/86291 http://hdl.handle.net/10220/45240 |
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