Unlike biological vision, most techniques for computer image processing are not robust over large samples of imagery. Natural systems seem unaffected by variation in local illumination and textures which interfere with conventional analysis. While change detection algorithms have been partially successful, many important tasks like extraction of roads and communication lines remain unsolved. The solution to these problems may lie in examining architectures and algorithms used by biological imaging systems. Pulsed oscillatory neural network design, based on biomemetics, seem to solve some of these problems. Pulsed oscillatory neural networks are examined for application to image analysis and segmentation of multispectral imagery from the Satellite Pour l'Observation de la Terre. Using biological systems as a model for image analysis of complex data, a pulsed coupled networks using an integrate and fire mechanism is developed. This architecture, based on layers of pulsed coupled neurons is tested against common image segmentation problems. Using a reset activation pulse similar to that generated by sacatic motor commands, an algorithm is developed which demonstrates the biological vision could be based on adaptive histogram techniques. This architecture is demonstrated to be both biologically plausible and more effective than conventional techniques. Using the pulse time-of-arrival as the information carrier, the image is reduced to a time signal, temporal encoding of imagery, which allows an intelligent filtering based on expectation. This technique is uniquely suited to multispectral/multisensor imagery and other sensor fusion problems.