Abstract
The aim of this letter is to present a spatio-temporal pixel-swapping algorithm (STPSA), based on conventional pixel-swapping algorithms (PSAs), in which both spatial and temporal contextual information from previous land cover maps or observed samples are well integrated and utilized to improve subpixel mapping accuracy. Unlike conventional pixel-swapping algorithms, STPSA is capable of utilizing prior information, which was previously ignored, to predict the attractiveness based on pairs of subpixels. This algorithm involves three main steps and operates in an iterative manner: 1) it predicts the maximum and minimum attractiveness of each pair of pixels; 2) ranks the swapping scores based on the attractiveness of all the pairs; and 3) swaps the locations of the pair of pixels with a maximum score to increase the objective function. Experiments with actual satellite images have demonstrated that the proposed algorithm performs better than other algorithms. In comparison, the proposed STPSA's better performance is due to the fact that prior information used in other algorithms is restricted to a percentage level rather than the real subpixel level.
Published Version
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