Fuzzy evaluated quantum cellular automata approach for watershed image analysis

Fuzzy approaches in a low-level image processing method to partition the homogeneous regions are important challenges in image segmentation. The analysis of the fuzziness in data produces comparable or improved solutions compared with the respective crisp approaches. The novel approach proposed in this chapter has been found to enhance the functionality of the fuzzy rule base and thus enhance the established potentiality of new fuzzy-based segmentation domain with the help of partitioned quantum cellular automata. Image segmentation among overlapping land cover areas on satellite images is a very crucial problem. To detect the belongingness is an important problem for mixed-pixel classification. This new approach to pixel classification is a hybrid method of fuzzy c-means and partitioned quantum cellular automata methods. This new unsupervised method is able to detect clusters using a two-dimensional partitioned cellular automaton model based on fuzzy segmentations. This method detects the overlapping areas in satellite images by analyzing uncertainties from fuzzy set membership parameters. As a discrete, dynamical system, a cellular automaton explores uniformly interconnected cells with states. In the second phase of our method, we use a two-dimensional partitioned quantum cellular automaton to prioritize allocations of mixed pixels among overlapping land cover areas. We tested our method on the Tilaiya Reservoir catchment area of the Barakar River for the first time. The clustered regions are compared with well-known fuzzy C-means and K-means methods and also with the ground truth information. The results show the superiority of our new method.

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