A statistical multiscale approach to image segmentation and fusion

We propose an algorithm to adaptively segment and fuse images by alternating wavelet packet and local cosine transforms each containing best basis selection and thresholding. Within segmented regions fusion is informed by multiple hypothesis testing based on a log-linear factorial model. This fusion identifies homogenous regions from which to select wavelet or local cosine packets, possibly from the original images. The successful performance of the fusion algorithm and segmentation is demonstrated on some multispectral thematic mapper imagery.