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\begin{abstract}  Unsupervised land-use/cover classification is of great interest, since it becomes even more difficult to obtain high-quality labeled data. Still considered one of the most used clustering techniques, the well-known $k$-means plays an important role in the pattern recognition community. Its simple formulation and good results in a number of applications have fostered the development of new variants and methodologies to address the problem of minimizing the distance from each dataset sample to its nearest centroid (mean). In this work, we present a Genetic Programming-based hyper-heuristic approach to combine different meta-heuristic techniques used to enhance $k$-means effectiveness. The proposed approach is evaluated in four satellite and two radar images, images  showing promising results. results, while outperforming each individual meta-heuristic technique.  \end{abstract}