In this paper, we present a new fusion algorithm based on a multidecomposition approach with the DFT based symmetric, zero-phase, nonoverlapping digital filter bank representation. The DFT of the signal is separated into two parts leading to the low and high −pass components then decimated by two to obtain subband signals. The original signal may be recovered by interpolating the subband signals, computing their inverse DFT and summing the results. In the proposed image fusion algorithm, two or more source images are decomposed into subbands by DFT based digital filters. The detail and approximation subband coefficients are modified according to their magnitudes and mean values, respectively. Then, the modified subbands are combined in the subband domain. Finally, the fused image is obtained by the inverse transform.
In this study, we investigate an unsupervised learning algorithm for the segmentation of remote sensing images in which the optimum number of clusters is automatically estimated, and the clustering quality is checked. The computational load is also reduced as compared to a single stage algorithm. The algorithm has two stages. At the first stage of the algorithm, the self-organizing map was used to obtain a large number of prototype clusters. At the second stage, these prototype clusters were further clustered with the K-means clustering algorithm to obtain the final clusters. A clustering validity checking method, Davies-Bouldin validity checking index, was used in the second stage of the algorithm to estimate the optimal number of clusters in the data set.