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Improving super-resolution mapping through combining multiple super-resolution land-cover maps

Li, Xiaodong; Ling, Feng; Foody, Giles M.; Du, Yun

Authors

Xiaodong Li

Feng Ling

GILES FOODY giles.foody@nottingham.ac.uk
Professor of Geographical Information

Yun Du



Abstract

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.

Citation

Li, X., Ling, F., Foody, G. M., & Du, Y. (2016). Improving super-resolution mapping through combining multiple super-resolution land-cover maps. International Journal of Remote Sensing, 37(10), 2415-2432. https://doi.org/10.1080/01431161.2016.1148288

Journal Article Type Article
Acceptance Date Dec 14, 2015
Online Publication Date May 6, 2016
Publication Date May 18, 2016
Deposit Date Apr 29, 2016
Publicly Available Date May 6, 2016
Journal International Journal of Remote Sensing
Print ISSN 0143-1161
Electronic ISSN 1366-5901
Publisher Taylor & Francis Open
Peer Reviewed Peer Reviewed
Volume 37
Issue 10
Pages 2415-2432
DOI https://doi.org/10.1080/01431161.2016.1148288
Keywords Super-resolution land-cover mapping; Mixed pixels; Voting
Public URL http://eprints.nottingham.ac.uk/id/eprint/32952
Publisher URL http://www.tandfonline.com/doi/full/10.1080/01431161.2016.1148288
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 06/05/2016, available online: http://www.tandfonline.com/10.1080/01431161.2016.1148288

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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