@inproceedings {595, title = {Efficient interpolation of large image sequences}, booktitle = {IEEE International Geoscience And Remote Sensing Symposium}, year = {2002}, address = {Toronto}, abstract = {

Dynamic estimation of large-scale remote-sensing image sequences is important in a variety of scientific applications. However, the growing size of such sensed images makes conventional dynamic estimation methods, for example the Kalman and related filters, impractical. In this paper we present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a large (512 times;512) image sequence of ocean surface temperature.

}, keywords = {dynamic estimation, geophysical signal processing, image sequences, interpolation, Kalman filter, Kalman filters, large image sequences, large-scale remote-sensing image sequences, ocean surface temperature, oceanographic techniques, Remote Sensing}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1027173}, author = {F M. Khellah and P Fieguth} } @article {587, title = {Mapping and pseudo-inverse algorithms for data assimilation}, journal = {IEEE International Geoscience and Remote Sensing Symposium}, volume = {6}, year = {2002}, pages = {3221 - 3223}, abstract = {

Among existing ocean data assimilation methodologies, reduced-state Kalman filters are a widely-studied compromise between resolution, optimality, error specification, and computational feasibility. In such reduced-state filters, the measurement update takes place on a coarser grid than that of the general circulation model (GCM); therefore, these filters require mapping operators from the GCM grid to the reduced state and vice-versa. The general requirements are that the state-reduction and interpolation operators be pseudo-inverses of each other, that the coarse state defines a closed dynamical system, that the mapping operations be insensitive to noise, and that they be appropriate for regions with irregular coastlines and bathymetry. In this paper we investigate a variety of approaches, including computing the pseudoinverse by brute force, using the FFT, subsampling methods, implicit methods, and finally develop a novel iterative approach. We also evaluate the mapping performance of eleven interpolation kernels; surprisingly, common kernels such as bilinear, exponential, Gaussian, and sinc, performed only moderately well. This comprehensive study greatly reduces the computational bottleneck and guesswork of pseudo-inverse algorithms, making possible the application of reduced-state filters to global problems at state-of-the-art resolution.

}, keywords = {bathymetry, closed dynamical system, coarse state, computational bottleneck, data assimilation, FFT, GCM grid, general circulation model, geophysical signal processing, implicit methods, interpolation, interpolation operators, irregular coastlines, iterative approach, Kalman filters, mapping algorithms, ocean data assimilation methodologies, oceanographic techniques, pseudo-inverse algorithms, reduced order systems, reduced-state filters, reduced-state Kalman filters, state-reduction, subsampling methods}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1027136}, author = {P Fieguth and D Menemenlis and I Fukumori} } @inproceedings {490, title = {A general multiresolution approach to the estimation of dense fields in remote sensing}, booktitle = {International Conference on Image Processing}, year = {1996}, address = {Lausanne, Switzerland}, abstract = {

A fast multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic data. This multiscale algorithm produces solution and error estimates which are consistent with those obtained from exact least-squares methods, but at a small fraction of the computational cost. Problems whose solution would be completely impractical using exact least-squares, that is problems with tens or hundreds of thousands of measurements and estimation grid points, can easily be solved on a small workstation using the multiscale algorithm. Contrary to methods previously proposed for solving large least-squares problems, the multiscale approach provides error statistics while permitting long-range correlations, using all measurements, and permitting arbitrary measurement locations

}, keywords = {arbitrary measurement locations, computational cost, correlation methods, dense fields estimation, error estimates, error statistics, exact least-squares methods, fast multiscale optimal interpolation algorithm, general multiresolution approach, geophysical signal processing, hydrographic data mapping, hydrological techniques, image resolution, interpolation, least squares approximations, long-range correlations, measurements, multiscale algorithm, oceanographic data mapping, oceanographic techniques, Remote Sensing, workstation}, doi = {http://dx.doi.org/10.1109/ICIP.1996.560937}, author = {P Fieguth and A S. Willsky and D Menemenlis and C Wunsch} } @inproceedings {454, title = {Multiresolution statistical analysis and assimilation of large ocean data sets}, booktitle = {20th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {1995}, abstract = {

A significant problem in oceanographic remote sensing is the dense gridding or smoothing of sparsely sampled altimetric data. The smoothing of altimetric measurements has an application much broader than just the regular production of elevation maps for oceanographers, however. In particular, the ability to estimate ocean circulation patterns from altimetric data can serve as an important measure for the verification of ocean acoustic tomographic results. The authors present a multiscale technique capable of extremely efficient interpolation of altimetric data: about 256000 estimates and estimation error variances are computed in one minute on a Sun Sparc-10. They also demonstrate how similar techniques may be used to directly estimate the surface gradient and biases in the geoid-model error

}, keywords = {assimilation, biases, elevation maps, estimation error, geodesy, geoid-model error, geophysical signal processing, gridding, interpolation, large ocean data sets, multiresolution statistical analysis, multiscale technique, ocean acoustic tomographic results, ocean circulation patterns, oceanographic remote sensing, oceanographic techniques, parameter estimation, radar altimetry, radar signal processing, remote sensing by radar, signal resolution, smoothing, smoothing methods, sparsely sampled altimetric data, statistical analysis, surface gradient}, doi = {http://dx.doi.org/10.1109/ICASSP.1995.479422}, author = {P Fieguth and W C Karl and A S Willsky} }