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

Li, Xiaodong and Ling, Feng and 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)

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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.

Item Type: Article
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:
Keywords: Super-resolution land-cover mapping; Mixed pixels; Voting
Schools/Departments: University of Nottingham UK Campus > Faculty of Social Sciences > School of Geography
Identification Number:
Depositing User: Eprints, Support
Date Deposited: 29 Apr 2016 13:40
Last Modified: 19 Sep 2016 13:09

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