@article {281, title = {Statistical fusion of two-scale images of porous media}, journal = {Advances in Water Resources}, volume = {32}, year = {2009}, pages = {1567 - 1579}, abstract = {

The reconstruction of the architecture of void space in porous media is a challenging task, since porous media contain pore structures at multiple scales. Whereas past methods have been limited to producing samples with matching statistical behavior, the patterns of grey-level values in a measured sample actually say something about the unresolved details, thus we propose a statistical fusion framework for reconstructing high-resolution porous media images from low-resolution measurements. The proposed framework is based on a posterior sampling approach in which information obtained by low-resolution (MRI or X-ray) measurements is combined with prior models inferred from high-resolution microscopic data, typically 2D. In this paper, we focus on two-scale reconstruction tasks in which the measurements resolve only the large scale structures, leaving the small-scale to be inferred. The evaluation of the results generated by the proposed method shows the strong ability of the proposed method in reconstructing fine-scale structures positively correlated with the underlying ground truth. Comparing our method with the recent method of Okabe and Blunt [12], in which the measurements are also used in the reconstruction, we conclude that our method is more robust to the resolution of the measurement, and more closely matches the underlying fine-scale field.

}, keywords = {Data fusion, magnetic resonance imaging, Porous media reconstruction, Posterior sampling, simulated annealing}, issn = {0309-1708}, doi = {http://dx.doi.org/10.1016/j.advwatres.2009.08.005}, url = {http://www.sciencedirect.com/science/article/pii/S0309170809001274}, author = {A Mohebi and P Fieguth and M A. Ioannidis} } @inproceedings {532, title = {Improved texture recognition of SAR sea ice imagery by data fusion of MRF features with traditional methods}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Image texture interpretation is an important aspect of the computer-assisted discrimination of SAR sea ice imagery. Co-occurrence probabilities are the most common approach to solve this problem. However, other texture feature extraction methods exist that have not been fully studied for their ability to interpret SAR sea ice imagery. Gabor filters and Markov random fields (MRF) are two such methods considered. Classification and significance level testing shows that co-occurrence probabilities classify the data with the highest classification rate, with Gabor filters a close second. MRF results significantly lag Gabor and co-occurrence results. However, the MRF features are uncorrelated with respect to co-occurrence and Gabor features. The fused co-occurrence/MRF feature set achieves higher performance

}, keywords = {classification rate, Data fusion, Gabor filters, image texture, image texture interpretation, Markov Random Fields, occurrence probabilities, oceanographic techniques, remote sensing by radar, SAR sea ice imagery, sea ice, sensor fusion, significance level testing, synthetic aperture radar, texture feature extraction methods, texture recognition}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.976781}, author = {D A. Clausi} } @inproceedings {500, title = {Data fusion of sea-surface temperature data}, booktitle = {IEEE Geoscience and Remote Sensing Society}, year = {2000}, abstract = {

The problem of data fusion, the merging of data taken by different sensors, becomes ever more relevant with the launch of each new remote-sensing platform. One example of considerable current interest in the climate change community is the production of an improved sea surface temperature (SST) map. In particular, two state-of-the-art instruments - the Along Track Scanning Radiometer (ATSR) and AVHRR - share complementary features: ATSR allows excellent cloud discrimination and atmospheric correction based on its dual-view scanning geometry, but observes only narrow swaths of ocean; AVHRR suffers from low-wavenumber atmospheric distortions and cloud contamination, but has extensive global coverage. The authors propose a methodology for combining ocean skin temperatures from the ATSR and AVHRR instruments to produce a continuous analysis at one-sixth degree spatial and three-day temporal resolutions, together with reliable error estimates, from the fusion of multiple datasets with arbitrary sampling characteristics, resulting in estimated temperatures which unite the precision of ATSR with the superior coverage afforded by AVHRR

}, keywords = {ATSR, AVHRR, coverage, Data fusion, geophysical signal processing, he Along Track Scanning Radiometer, infrared radiometry, IR radiometry, measurement technique, multiple dataset, multispectral remote sensing, ocean, ocean skin temperature, oceanographic techniques, precision, Remote Sensing, sea surface temperature, sensor fusion, SST, visible}, doi = {http://dx.doi.org/10.1109/IGARSS.2000.858311}, author = {P Fieguth and F M. Khellah and M J. Murray and M R. Allen} }