Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types some of which cover significantly large areas, while some (e.g., bridges and roads) occupy relatively much smaller regions. Detecting regions or clusters of such widely varying sizes presents a challenging task. A modified differential evolution based fuzzy clustering technique, is proposed in this article. Real-coded encoding of the cluster centres is used for this purpose. Results demonstrating the effectiveness of the proposed technique are provided for several synthetic and real life data sets as well as for some benchmark functions. Different landcover regions in remote sensing imagery have also been classified using the proposed technique to establish its efficiency. Statistical significance tests have been performed to establish the superiority of the proposed algorithm.

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