Multiple sensor image registration, image fusion and dimension reduction of Earth science imagery

The goal of our project is to develop and evaluate image analysis methodologies for use on the ground or on-board spacecraft particularly spacecraft constellations. Our focus is on developing methods to perform automatic registration and fusion of multisensor data representing multiple spatial, spectral and temporal resolutions, as well as dimension reduction of hyperspectral data. Feature extraction methods such as wavelet decomposition, edge detection and mutual information are combined with feature matching methods such as cross-correlation, optimization, and statistically robust techniques to perform image registration. The approach to image fusion is application-based and involves wavelet decomposition, dimension reduction, and classification methods. Dimension reduction is approached through novel methods based on principal component analysis and wavelet decomposition, and implemented on Beowulf-type parallel architectures. Registration algorithms are tested and compared on several multi-sensor datasets, including one of the EOS Core Sites, the Konza Prairie in Kansas, utilizing four different sensors: IKONOS, Landsat-7/ETM+, MODIS, and SeaWIFS. Fusion methods are tested using Landsat, MODIS and SAR or JERS data. Dimension reduction is demonstrated on A VIRIS hyperspectral data.