An iterative interpolation deconvolution algorithm for superresolution land cover mapping
Super-resolution mapping (SRM) is a method to produce a fine spatial resolution land cover map from coarse spatial resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation, and then...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2016
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| Online Access: | https://eprints.nottingham.ac.uk/37923/ |
| _version_ | 1848795561405186048 |
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| author | Ling, Feng Foody, Giles M. Ge, Yong Li, Xiaodong Du, Yun |
| author_facet | Ling, Feng Foody, Giles M. Ge, Yong Li, Xiaodong Du, Yun |
| author_sort | Ling, Feng |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Super-resolution mapping (SRM) is a method to produce a fine spatial resolution land cover map from coarse spatial resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation, and then determines class labels of fine resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between observed coarse resolution fraction images and the latent fine resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms, and should be replaced by de-convolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation de-convolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse resolution fraction images with an area-to-area interpolation algorithm, and produces an initial fine resolution land cover map by de-convolution. The fine spatial resolution land cover map is then updated by re-convolution, back-projection and de-convolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multi-spectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors, and can preserve the patch continuity and the patch boundary smoothness, simultaneously. Moreover, the IID algorithm produced fine resolution land cover maps with higher accuracies than those produced by other SRM algorithms. |
| first_indexed | 2025-11-14T19:34:03Z |
| format | Article |
| id | nottingham-37923 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:34:03Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-379232020-05-04T18:23:38Z https://eprints.nottingham.ac.uk/37923/ An iterative interpolation deconvolution algorithm for superresolution land cover mapping Ling, Feng Foody, Giles M. Ge, Yong Li, Xiaodong Du, Yun Super-resolution mapping (SRM) is a method to produce a fine spatial resolution land cover map from coarse spatial resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation, and then determines class labels of fine resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between observed coarse resolution fraction images and the latent fine resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms, and should be replaced by de-convolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation de-convolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse resolution fraction images with an area-to-area interpolation algorithm, and produces an initial fine resolution land cover map by de-convolution. The fine spatial resolution land cover map is then updated by re-convolution, back-projection and de-convolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multi-spectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors, and can preserve the patch continuity and the patch boundary smoothness, simultaneously. Moreover, the IID algorithm produced fine resolution land cover maps with higher accuracies than those produced by other SRM algorithms. Institute of Electrical and Electronics Engineers 2016-12-31 Article PeerReviewed Ling, Feng, Foody, Giles M., Ge, Yong, Li, Xiaodong and Du, Yun (2016) An iterative interpolation deconvolution algorithm for superresolution land cover mapping. IEEE Transactions on Geoscience and Remote Sensing, 54 (12). pp. 7210-7222. ISSN 0196-2892 Interpolation De-convolution Super-resolution Mapping http://ieeexplore.ieee.org/document/7553472/ doi:10.1109/TGRS.2016.2598534 doi:10.1109/TGRS.2016.2598534 |
| spellingShingle | Interpolation De-convolution Super-resolution Mapping Ling, Feng Foody, Giles M. Ge, Yong Li, Xiaodong Du, Yun An iterative interpolation deconvolution algorithm for superresolution land cover mapping |
| title | An iterative interpolation deconvolution algorithm for superresolution land cover mapping |
| title_full | An iterative interpolation deconvolution algorithm for superresolution land cover mapping |
| title_fullStr | An iterative interpolation deconvolution algorithm for superresolution land cover mapping |
| title_full_unstemmed | An iterative interpolation deconvolution algorithm for superresolution land cover mapping |
| title_short | An iterative interpolation deconvolution algorithm for superresolution land cover mapping |
| title_sort | iterative interpolation deconvolution algorithm for superresolution land cover mapping |
| topic | Interpolation De-convolution Super-resolution Mapping |
| url | https://eprints.nottingham.ac.uk/37923/ https://eprints.nottingham.ac.uk/37923/ https://eprints.nottingham.ac.uk/37923/ |