Super-resolution Target Mapping from Soft-Classified Remotely Sensed Imagery

A simple, efficient algorithm is presented for super-resolution target mapping from remotely sensed images. Following an initial random allocation of pixel proportions to binary 'hard' sub-pixel classes, the algorithm works in a series of iterations, each of which contains two stages. For each iteration, a distance weighted function of neighbouring pixels is computed for all sub-pixels. Then, on a pixel-by-pixel basis, the '1' with the minimum value of the function is swapped with the '0' with the maximum value of the function, if the swap results in an increase in some objective function. The algorithm is demonstrated to work reasonably well with simple images, opening the way for further research to explore the algorithm, to extend the algorithm to multiple classes and to develop more efficient, but equally simple algorithms.