Proceedings of SPIE - the International Society for Optical Engineering
Many sensor systems are available for sensing the earth surface from satellites as well as airborne and mobile platforms. Thus, fusing data from multiple sensors is becoming a common theme in earth remote sensing. A major goal of remote sensing image fusion is resolution enhancement. In this paper, optimization techniques are presented and discussed in order to help make an image fusion process a practical method for not only spectral signature based image analysis but also for algorithm development in remote sensing of water. The technique described and explored in this paper includes the identification of feature areas, stratified random pixel selection, singular value decomposition model building for synthetic image generation, and optimization of the 2D Butterworth filter cutoff and order coefficients in a spectral and spatial resolution enhancement protocol. The process is also called spatial sharpening of hyperspectral imagery as presented in this paper. Examples of methods for estimating errors in the data fusion process are also described using coastal littoral zone remote sensing imagery with an emphasis on weathered oil scenes. The optimization and testing of a data fusion methodology or protocol described utilizes image to image georeferencing methods, nearest neighborhood and linear remapping of multi-resolution spatial and spectral imagery. The central optimization procedures entails random selection of pixels from feature areas in simultaneously acquired multispectral and hyperspectral scenes in order to build multiple "SVD" singular value decomposition models and optimized selection of these image models for each hyperspectral channel based upon the non-parametric K-S p-statistical test. The model synthetic imagery is then used with the 2D discrete cosine and inverse cosine filters, a 2D Butterworth filter. Optimization of the 2D Butterworth filter cutoff and order coefficients are conducted for each hyperspectral band and these coefficients are optimized using the same K-S based tests. The above optimization protocol results in synthetic reflectance hyperspectral cube where minimization between observed and synthetic hyperspectral signatures has been performed for each hyperspectral channel. Results indicate the synthetic hyperspectral resolution enhancement methodology is most sensitive to (a) the pixels selected (from feature areas) for use in the SVD model building process and (b) the 2D Butterworth cutoff frequency selected.
Bostater, C. R. (2012). Resolution enhancement optimizations for hyperspectral and multispectral synthetic image fusion. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 8532 doi:10.1117/12.974596