This paper presents a novel unsupervised clustering scheme to find changes in two or more coregistered remote sensing images acquired at different times. This method is able to find nonlinear boundaries to the change detection problem by exploiting a kernel-based clustering algorithm. The kernel k-means algorithm is used in order to cluster the two groups of pixels belonging to the 'change' and 'no change' classes (binary mapping). In this paper, we provide an effective way to solve the two main challenges of such approaches: i) the initialization of the clustering scheme and ii) a way to estimate the kernel function hyperparameter(s) without an explicit training set. The former is solved by initializing the algorithm on the basis of the Spectral Change Vector (SCV) magnitude and the latter is optimized by minimizing a cost function inspired by the geometrical properties of the clustering algorithm. Experiments on VHR optimal imagery prove the consistency of the proposed approach.