A multiobjective simulated annealing based fuzzy-clustering technique with symmetry for pixel classification in remote sensing imagery

An important approach for landcover classification in remote sensing images is by clustering the pixels in the spectral domain into several fuzzy partitions. In this article the problem of fuzzy clustering is posed as one of searching for some suitable number of cluster centers so that some measures of validity of the obtained partitions should be optimized. In this paper a recently developed multiobjective simulated annealing based technique, AMOSA (archived multiobjective simulated annealing technique), is used to perform clustering, taking two validity measures as two objective functions. Here two fuzzy cluster validity functions namely, well-known XB-index and newly developed FSym-index are optimized simultaneously to automatically evolve the appropriate number of clusters present in an image. Thus the proposed algorithm provides a set of final non-dominated solutions, which the user can judge relatively and pick up the most promising one according to the domain requirement. The effectiveness of this proposed clustering technique in comparison with the existing fuzzy C-means clustering is shown for automatically classifying two remote sensing satellite images of the parts of the cities of Kolkata and Mumbai.