Improving super-resolution mapping through combining multiple super-resolution land-cover maps
Super-resolution mapping (SRM) is an ill-posed problem, and different SRM algorithms may generate non-identical fine spatial resolution land-cover maps (sub-pixel maps) from the same input coarse spatial resolution image. The output sub-pixels maps may each have differing strengths and weaknesses. A...
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| Format: | Article |
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Taylor and Francis
2016
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| Online Access: | https://eprints.nottingham.ac.uk/32952/ |
| _version_ | 1848794526951407616 |
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| author | Li, Xiaodong Ling, Feng Foody, Giles M. Du, Yun |
| author_facet | Li, Xiaodong Ling, Feng Foody, Giles M. Du, Yun |
| author_sort | Li, Xiaodong |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Super-resolution mapping (SRM) is an ill-posed problem, and different SRM algorithms may generate non-identical fine spatial resolution land-cover maps (sub-pixel maps) from the same input coarse spatial resolution image. The output sub-pixels maps may each have differing strengths and weaknesses. A multiple SRM (M-SRM) method that combines the sub-pixel maps obtained from a set of SRM analyses, obtained from a single or multiple set of algorithms, is proposed in this study. Plurality voting, which selects the class with the most votes, is used to label each sub-pixel. In this study, three popular SRM algorithms, namely, the pixel swapping algorithm (PSA), the Hopfield neural network (HNN) algorithm, and Markov random field (MRF) based algorithm, were used. The proposed M-SRM algorithm was validated using two data sets: a simulated multi-spectral image and an airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral image. Results show that the highest overall accuracies were obtained by M-SRM in all experiments. For example, in the AVIRIS image experiment, the highest overall accuracies of PSA, HNN and MRF were 88.89%, 93.81% and 82.70% respectively, and increased to 95.06%, 95.37% and 85.56% respectively for M-SRM obtained from the multiple PSA, HNN and MRF analyses. |
| first_indexed | 2025-11-14T19:17:36Z |
| format | Article |
| id | nottingham-32952 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:17:36Z |
| publishDate | 2016 |
| publisher | Taylor and Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-329522020-05-04T17:52:55Z https://eprints.nottingham.ac.uk/32952/ Improving super-resolution mapping through combining multiple super-resolution land-cover maps Li, Xiaodong Ling, Feng Foody, Giles M. Du, Yun Super-resolution mapping (SRM) is an ill-posed problem, and different SRM algorithms may generate non-identical fine spatial resolution land-cover maps (sub-pixel maps) from the same input coarse spatial resolution image. The output sub-pixels maps may each have differing strengths and weaknesses. A multiple SRM (M-SRM) method that combines the sub-pixel maps obtained from a set of SRM analyses, obtained from a single or multiple set of algorithms, is proposed in this study. Plurality voting, which selects the class with the most votes, is used to label each sub-pixel. In this study, three popular SRM algorithms, namely, the pixel swapping algorithm (PSA), the Hopfield neural network (HNN) algorithm, and Markov random field (MRF) based algorithm, were used. The proposed M-SRM algorithm was validated using two data sets: a simulated multi-spectral image and an airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral image. Results show that the highest overall accuracies were obtained by M-SRM in all experiments. For example, in the AVIRIS image experiment, the highest overall accuracies of PSA, HNN and MRF were 88.89%, 93.81% and 82.70% respectively, and increased to 95.06%, 95.37% and 85.56% respectively for M-SRM obtained from the multiple PSA, HNN and MRF analyses. Taylor and Francis 2016-05-06 Article PeerReviewed Li, Xiaodong, Ling, Feng, Foody, Giles M. and Du, Yun (2016) Improving super-resolution mapping through combining multiple super-resolution land-cover maps. International Journal of Remote Sensing, 37 (10). pp. 2415-2432. ISSN 1366-5901 (In Press) Super-resolution land-cover mapping; Mixed pixels; Voting http://www.tandfonline.com/doi/full/10.1080/01431161.2016.1148288 doi:10.1080/01431161.2016.1148288 doi:10.1080/01431161.2016.1148288 |
| spellingShingle | Super-resolution land-cover mapping; Mixed pixels; Voting Li, Xiaodong Ling, Feng Foody, Giles M. Du, Yun Improving super-resolution mapping through combining multiple super-resolution land-cover maps |
| title | Improving super-resolution mapping through combining
multiple super-resolution land-cover maps |
| title_full | Improving super-resolution mapping through combining
multiple super-resolution land-cover maps |
| title_fullStr | Improving super-resolution mapping through combining
multiple super-resolution land-cover maps |
| title_full_unstemmed | Improving super-resolution mapping through combining
multiple super-resolution land-cover maps |
| title_short | Improving super-resolution mapping through combining
multiple super-resolution land-cover maps |
| title_sort | improving super-resolution mapping through combining
multiple super-resolution land-cover maps |
| topic | Super-resolution land-cover mapping; Mixed pixels; Voting |
| url | https://eprints.nottingham.ac.uk/32952/ https://eprints.nottingham.ac.uk/32952/ https://eprints.nottingham.ac.uk/32952/ |