In this letter, an unsupervised kernel-based approach to change detection is introduced. Nonlinear clustering is utilized to partition in two a selected subset of pixels representing both changed and unchanged areas. Once the optimal clustering is obtained, the learned representatives of each group are exploited to assign all the pixels composing the multitemporal scenes to the two classes of interest. Two approaches based on different assumptions of the difference image are proposed. The first accounts for the difference image in the original space, while the second defines a mapping describing the difference image directly in feature spaces. To optimize the parameters of the kernels, a novel unsupervised cost function is proposed. An evidence of the correctness, stability, and superiority of the proposed solution is provided through the analysis of two challenging change-detection problems.