@inproceedings {370, title = {Toward an optimal solution for multitarget tracking}, booktitle = {2007 IEEE International Conference on Image Processing}, year = {2007}, abstract = {

There are ever increasing number of applications of multi-target tracking and considerable research has been conducted to solve this problem. Multi-target tracking is a NP-hard problem and almost all of the present multi-target tracking algorithms are sub-optimal by finding the solution in a reduced hypothesis space. In this paper we introduce a new approach toward finding the optimal single frame solution for general multi-target tracking problem. Our proposed method finds the optimal solution using linear programming optimization method. The proposed method has been successfully applied to synthetic and real data.

}, keywords = {computational complexity, joint probabilistic data association, linear programming, multitarget tracking, NP-hard problem, optimal solution, optimization method, probability, sensor fusion, target tracking}, doi = {http://dx.doi.org/10.1109/ICIP.2007.4379588}, author = {N Nezamoddin Kachouie and P Fieguth} } @inproceedings {593, title = {Automatic registration of SAR and visible band remote sensing images}, booktitle = {Joint IEEE International Geoscience and Remote Sensing Symposium and 24th Canadian Remote Sensing Symposium}, year = {2002}, abstract = {

Image registration is one of the basic image processing operations in remote sensing. With an increasing number of images collected every day from different sensors, automated registration of multi-sensor/multi-spectral images has become an important issue. A wide range of registration techniques exists for different types of applications and data sources, however no algorithm is known that can accurately register multi-source images consistently. This research addresses this problem by investigating the development of a fully automatic registration system for synthetic aperture radar (SAR) and optical remote sensing images. The development of this new automatic image registration method is based on the extraction and matching of common features that are visible in both images. The algorithm involves the following five steps: noise removal, edge extraction, edge linking pattern extraction and pattern matching. The application of the developed automatic image registration model to SAR and optical image pairs showed that accurate ground control points (GCPs) could be identified automatically.

}, keywords = {algorithm, edge extraction, edge linking pattern extraction, fully automatic registration, geophysical measurement technique, geophysical signal processing, geophysical techniques, image registration, land surface, multi-source image, noise removal, optical imaging, pattern matching, radar imaging, radar remote sensing, Remote Sensing, remote sensing by radar, remote sensing image, SAR, sensor fusion, synthetic aperture radar, terrain mapping, visible band}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026106}, author = {M Ali and D A. Clausi} } @inproceedings {594, title = {Data fusion of SSM/I channels using multiresolution wavelet transform}, booktitle = {IEEE International Geoscience And Remote Sensing Symposium}, year = {2002}, abstract = {

This paper presents an approach to the fusion of SSM/I (Special Sensor Microwave/Imager) data from different resolutions, based on the prior statistical information about the data. The result is an estimated field that lives in a finer scale than any of the measurements. We apply a Wavelet Transform that increases speed and decreases memory requirements by sparsifying and preconditioning the statistics. This approach makes feasible the use of reprogrammable FPGA implementations for onboard satellite data processing, which greatly enhances flexibility and, most importantly, reduces communication burdens by limiting the extent to which raw, unprocessed data are transmitted to the ground.

}, keywords = {19 to 85.5 GHz, atmosphere, atmospheric techniques, EHF, geophysical measurement technique, geophysical signal processing, geophysical techniques, image fusion, land surface, meteorology, microwave radiometry, multiresolution wavelet transform, ocean, oceanographic techniques, preconditioning, prior statistical information, satellite remote sensing, sea surface, sensor fusion, SHF, sparsifying, Special Sensor Microwave/Imager, SSM/I, statistics, terrain mapping, wavelet transforms}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026564}, author = {V K. Mehta and C M. Hammock and P Fieguth and H Krim} } @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} }