The goal of this project is to develop new approaches and innovative ideas, which are needed to represent oceanographic satellite imagery using data structures of software architecture that facilitate storage, transmission, processing throughput and accuracy of automated analysis results. Wavelet representation provides a unified framework for techniques including image features analysis, image compression and recognition. Wavelet bases are more attractive than traditional hierarchical bases because they are orthonormal, linear, continuous, and continuously invertible. Choosing wavelets that are simultaneously localized in both space and frequency, and decomposing a signal into a multiscale hierarchical basis with orientation selectivity, can provide a powerful methodology for automated analysis. Neural network will be used in pattern classification for the wavelet representations. The research of this proposal, which takes advantage of the latest image analysis and recognition methods, wavelet transform and neural network, shall lay a solid foundation for the development of an image analysis workstation for oceanographic satellite imagery. The first objective of our investigation is to identify those methods of wavelet representation that perform best in terms of interpretation and automated analysis. The second objective is to develop an image analysis workstation which integrates the most effective image interpretation methods and most advanced image processors for the oceanographic satellite imagery analysis.
