Abstract
ABSTRACTSuper-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.
Highlights
Super-resolution mapping (SRM) is a process used to predict the spatial distribution of land-cover classes in image pixels at a finer spatial resolution than that of the input data
The sub-pixel maps obtained from the pixel swapping algorithm (PSA), Hopfield neural network (HNN) and Markov random field (MRF) with the highest overall accuracies as well as those produced by the pixel-based multiple SRM (M-SRM) and the context-based M-SRM with highest accuracy for each analysis are shown in figure 2
It was evident that parts of the path were poorly represented, with some sections disconnected in the PSA, HNN and MRF results
Summary
Super-resolution mapping (SRM) is a process used to predict the spatial distribution of land-cover classes in image pixels at a finer spatial resolution than that of the input data. SRM has an important role to play in reducing the mixed pixel problem that is commonly encountered in mapping land-cover from remotely sensed data. Previous studies have shown that a varied set of land-cover representations may arise from the same coarse spatial resolution image through the use of different SRM methods (Foody and Doan 2007; Makido, Messina, and Shortridge 2008). The identification of an optimal SRM method in advance is a difficult, if not impossible, challenge
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
