Super-resolution mapping using cellular automata model

Abstract

Abstract Super-resolution mapping (SRM) is a category of techniques that aim to estimate fine-scale land cover maps from coarse spatial resolution remote sensing images. The main limitations of SRM methods are high computational complexity, demanding training data and parameter tuning. To overcome these drawbacks, this paper proposes a cellular automata (CA) based SRM (SRM-CA) approach. CA is adopted as it is a fast and efficient technique that incorporates simple rules about spatial adjacency effects. In the first step of SRM-CA, the proportions of endmembers were computed, to generate SR map the pure pixels were then mapped. To assign an appropriate label for unlabeled sub-pixels; the energy function was computed. Each given sub-pixel was then assigned to a class with maximum amount of the energy. Two synthetic imageries, namely circle and concentric circles images, and an orthophoto map from the city Centre of Vaihingen, Germany were tested for validation and comparison. The average computed Percent Correct Classified (PCC’) index for Vaihingen dataset was 98.52%. Moreover, in the case of employed circle synthetic dataset, the comparison of the results between SRM-CA technique and SRM Using Neural Network Predicted Wavelet Coefficients model illustrated that there are no differences between PCC and Kappa coefficient. Regarding concentric circles, SRM-CA approach outperforms BPFM model with gains of 99.91% in Kappa metric. Meanwhile, the proposed method requires less than 50 seconds computation time for Vaihingen data set which considerably less than other state-of-the-art SRM methods, and hence SRM-CA approach provides a new solution to sub-pixel land cover mapping.