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The problem of classifying an image into different homogeneous regions is viewed as a task of clustering the pixels in the intensity space. In this letter, a newly developed genetic clustering technique is used for automatically segmenting remote sensing satellite images. Each cluster is divided into several small hyperspherical subclusters, and the centers of all these small subclusters are encoded in a chromosome to represent the whole clustering. For assigning points to different clusters, these local subclusters are considered individually. For the purpose of objective function evaluation, these subclusters are merged appropriately to form a variable number of global clusters. A newly proposed point-symmetry-distance-based cluster validity index, Sym index, is used as a measure of the validity of the corresponding segment. The effectiveness of the proposed technique compared to a fuzzy C-means clustering technique, a recently proposed GAPS clustering with Sym-index-based method, and a subtractive clustering technique is demonstrated in identifying different land cover regions from two numeric image data sets and a remote sensing image of a part of the city of Kolkata.