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• W2342474866 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 the Markov random field (MRF)-based algorithm, were used. The proposed M-SRM algorithm was validated using two data sets: a simulated multispectral 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 these increased to 95.06, 95.37, and 85.56%, respectively for M-SRM obtained from the multiple PSA, HNN, and MRF analyses." @default.
• W2342474866 created "2016-06-24" @default.
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• W2342474866 date "2016-05-06" @default.
• W2342474866 modified "2023-09-26" @default.
• W2342474866 title "Improving super-resolution mapping through combining multiple super-resolution land-cover maps" @default.
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• W2342474866 doi "https://doi.org/10.1080/01431161.2016.1148288" @default.
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