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

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

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 and Francis
• 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: https://nottingham-repository.worktribe.com/output/791129
• Publisher URL: http://www.tandfonline.com/doi/full/10.1080/01431161.2016.1148288
• 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
• Contract Date: Apr 29, 2016