@inproceedings {287, title = {IceSynth: An image synthesis system for sea-ice segmentation evaluation}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

An ongoing challenge in automatic sea-ice monitoring using synthetic aperture radar (SAR) is the automatic segmentation of SAR sea-ice images based on the underlying ice type. Given the intractability of obtaining ground-truth segmentation data from polar regions, the evaluation of automatic SAR sea-ice image segmentation algorithms is generally limited to tests using real SAR imagery based on pseudo-ground truth data (e.g., manual segmentations) and simple synthetic tests using basic shape primitives. As such, it is difficult to evaluate automatic segmentation algorithms in a systematic and reliable manner using realistic scenarios. To tackle this issue, a novel image synthesis system named IceSynth is presented, which is capable of generating a variety of synthetic sea-ice images that are representative of real SAR sea-ice imagery. In IceSynth, SAR sea-ice textures for each ice type are synthesized via stochastic sampling based on non-parametric local conditional texture probability distribution estimates. A stochastic sampling approach based on non-parametric local class probability distribution estimates is used to generate large-scale sea-ice structures of various ice types based on ice classification priors extracted from real SAR sea-ice imagery. Experimental results show that IceSynth is capable of generating realistic-looking SAR sea-ice images that are well-suited for performing objective evaluation of SAR sea-ice image segmentation algorithms.

}, keywords = {automatic sea-ice monitoring, ice classification, ice types, IceSynth, image classification, image segmentation, image synthesis system, image texture, oceanographic techniques, polar regions, probability, probability distribution estimation, pseudo-ground truth data, remote sensing by radar, SAR imagery, sea ice, sea-ice segmentation evaluation, sea-ice structures, sea-ice textures, stochastic processes, stochastic sampling, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/CRV.2009.27}, author = {A Wong and W Zhang and D A. Clausi} } @inproceedings {290, title = {SEC: Stochastic ensemble consensus approach to unsupervised SAR sea-ice segmentation}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

The use of synthetic aperture radar (SAR) has become an integral part of sea-ice monitoring and analysis in the polar regions. An important task in sea-ice analysis is to segment SAR sea-ice imagery based on the underlying ice type, which is a challenging task to perform automatically due to various imaging and environmental conditions. A novel stochastic ensemble consensus approach to sea-ice segmentation (SEC) is presented to tackle this challenging task. In SEC, each pixel in the SAR sea-ice image is assigned an initial sub-class based on its tonal characteristics. Ensembles of random samples are generated from a random field representing the SAR sea-ice imagery. The generated ensembles are then used to re-estimate the sub-class of the pixels using a weighted median consensus strategy. Based on the probability distribution of the sub-classes, an expectation maximization (EM) approach is utilized to estimate the final class likelihoods using a Gaussian mixture model (GMM). Finally, maximum likelihood (ML) classification is performed to estimate the final class of each pixel within the SAR sea-ice imagery based on the estimated GMM and the assigned sub-classes. SEC was tested using a variety of operational RADARSAT-1 and RADARSAT-2 SAR sea-ice imagery provided by the Canadian Ice Service (CIS) and was shown to produce successfully segmentation results that were superior to approaches based on K-means clustering, Gamma mixture models, and Markov Random Field (MRF) models for sea-ice segmentation.

}, keywords = {Canadian Ice Service, expectation maximization approach, Gaussian mixture model, GMM, image segmentation, K-means clustering, Markov Random Field models, maximum likelihood classification, maximum likelihood estimation, oceanographic techniques, polar regions, probability, probability distribution, RADARSAT-1 imagery, RADARSAT-2 imagery, random samples, remote sensing by radar, SAR sea-ice imagery, sea ice, sea-ice analysis, sea-ice monitoring, stochastic ensemble consensus approach, synthetic aperture radar, tonal characteristics, underlying ice type, unsupervised SAR sea-ice segmentation, weighted median consensus strategy}, doi = {http://dx.doi.org/10.1109/CRV.2009.25}, author = {A Wong and D A. Clausi and P Fieguth} } @article {357, title = {SAR sea-ice image analysis based on iterative region growing using semantics}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {45}, year = {2007}, pages = {3919 - 3931}, abstract = {

Synthetic aperture radar (SAR) has been intensively used for sea-ice monitoring in polar regions. A computer-assisted analysis of SAR sea-ice imagery is extremely difficult due to numerous imaging parameters and environmental factors. This paper presents a system which, with some limited information provided, is able to perform an automated segmentation and classification for the SAR sea-ice imagery. In the system, both the segmentation and classification processes are based on a Markov random-field theory and are formulated in a joint manner under the Bayesian framework. Solutions to the formulation are obtained by a region-growing technique which keeps refining the segmentation and producing semantic class labels at the same time in an iterative manner. The algorithm is a general-segmentation approach named iterative region growing using semantics, which, in this paper, is dedicated to the problem of classifying the operational SAR sea-ice imagery provided by the Canadian Ice Service (CIS). The classified image results have been validated by the CIS personnel, and the resulting classifications are quite successful using the same algorithm applied to diverse data sets.

}, keywords = {automated segmentation, Bayes methods, Bayesian framework, Canadian Ice Service, iterative region growing technique, Markov random-field theory, oceanographic techniques, polar regions, remote sensing by radar, sea ice, sea-ice image analysis, sea-ice monitoring, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/TGRS.2007.908876}, author = {Q Yu and D A. Clausi} } @article {383, title = {Filament preserving model (FPM) segmentation applied to SAR sea-ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {44}, year = {2006}, pages = {3687 - 3694}, abstract = {

Modeling spatial context constraints using a Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to synthetic aperture radar (SAR) sea-ice segmentation has also been demonstrated recently. However, most existing MRF models are not capable of preserving filaments, specifically leads and ridges for SAR sea ice, which are valuable for ship navigation applications and necessary for identifying certain ice types. In this paper, a new statistical context model is proposed that, within the same scene, can simultaneously preserve narrow elongated features while producing similar smooth segmentation results comparable to typical MRF-based approaches. Tested on one synthetic image and two SAR sea-ice scenes, this filament preserving model substantially improves classification accuracies when compared to standard Gaussian mixture and MRF-based segmentation algorithms

}, keywords = {egg code, filament preserving model, image classification, image segmentation, Markov processes, Markov random field, MRF-based segmentation algorithm, noisy image segmentation, oceanographic techniques, remote sensing by radar, SAR sea-ice imagery, sea ice, ship navigation, standard Gaussian mixture, synthetic aperture radar, synthetic image}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2006.885046}, author = {Q Yu and D A. Clausi} } @inproceedings {393, title = {Filament preserving segmentation for SAR sea ice imagery using a new statistical model}, booktitle = {18th International Conference on Pattern Recognition (ICPR)}, year = {2006}, month = {08/2006}, address = {Hong Kong}, abstract = {

Modelling spatial context constraints using Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to SAR sea ice segmentation has also been demonstrated by Deng and Clausi (2005). However, most existing MRF models are not capable of preserving filaments, specifically leads and ridges for SAR sea ice, which are valuable for ship navigation applications and helpful for identifying certain ice types. A new statistical context model is proposed that can preserve such narrow elongated features while producing similar smooth segmentation results as those of existing MRF based approaches

}, keywords = {Feature Extraction, filament preserving segmentation, geophysical signal processing, Image Denoising, image segmentation, Markov processes, Markov random field, narrow elongated features, noisy image segmentation, oceanographic techniques, radar imaging, SAR sea ice imagery, sea ice, ship navigation, spatial context constraints, statistical analysis, statistical model, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/ICPR.2006.561}, author = {Q Yu and D A. Clausi} } @article {417, title = {Operational map-guided classification of SAR sea ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {43}, year = {2005}, pages = {2940 - 2951}, abstract = {

This paper presents a map-guided sea ice classification system built to work in parallel with the Canadian Ice Service (CIS) operations to produce pixel-based ice maps that complement actual \"egg code\" maps produced by CIS. The system uses the CIS maps as input to guide classification by providing information on the number of ice types and their final label for specific regions. Segmentation is based on a modified adaptive Markov random field (MRF) model that uses synthetic aperture radar (SAR) intensities and texture features as input. The ice type labeling is performed automatically by gathering evidences based on a priori information on one or two classes and deducing the other labels iteratively by comparing distributions of segments. Three methods for comparing the segment distributions (Fisher criterion, Mahalanobis distance, and Kolmogorov-Smirnov test) were implemented and compared. The system is fully described with special attention to the labeling procedure. Examples are presented in the form of two CIS SAR-based ice maps from the Gulf of Saint Lawrence region and one example from the Beaufort Sea. The results indicate that when the segmentation is good, the labeling attains best results (between 71\% and 89\%) based on evaluation by a sea ice analyst. Some problems remain to be assessed which are primarily attributable to discrepancies in the information provided by the egg code and what is actually visible in the SAR image. Subscale information on floe size and shape available to human analysts, but not in this classification system, also appear to be a critical information for separating some ice types.

}, keywords = {Beaufort Sea, Canadian Ice Service, Fisher criterion, geophysical signal processing, gray-level cooccurrence matrix, Gulf of Saint Lawrence, image classification, image segmentation, image texture, Kolmogorov-Smirnov test, Mahalanobis distance, map-guided classification, Markov processes, Markov random field, oceanographic regions, oceanographic techniques, pixel-based ice maps, radar imaging, remote sensing by laser beam, remote sensing by radar, SAR sea ice imagery, sea ice, sea ice classification, segment distribution comparison, synthetic aperture radar}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2005.857897}, author = {P Maillard and D A. Clausi and H Deng} } @article {813, title = {A comparison of two 85-GHz SSM/I ice concentration algorithms with AVHRR and ERS-2 SAR imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {41}, year = {2003}, pages = {2294 - 2306}, abstract = {

Sea ice concentrations obtained with two algorithms from Special Sensor Microwave/Imager (SSM/I) data are compared to spaceborne visible/infrared and active microwave imagery for the Greenland Sea in spring. Both algorithms, the ARTIST Sea Ice algorithm (ASI) and the SEA LION algorithm (SLA), utilize 85-GHz SSM/I brightness temperatures with a spatial resolution of 15 km times;13 km. Ice concentrations obtained from Advanced Very High Resolution Radiometer (AVHRR) infrared data in cloud-free areas are underestimated by SLA and ASI ice concentrations by 3.6\% and 8.3\% (correlation coefficients of 0.90 and 0.91). Ice concentrations estimated from texture classified ERS-2 synthetic aperture radar (SAR) images by assigning experience-based ice concentrations to ice-type classes are overestimated by SLA and ASI ice concentrations by 4.4\% and 1.5\% (correlation coefficients of 0.84 and 0.77). However, omitting low/high ice concentrations forming up to 80\% (AVHRR) and 60\% (SAR) of the entire dataset reveals a significantly different statistic. For instance, the correlation between AVHRR and SLA and ASI ice concentrations drops to 0.77 and 0.70, respectively. All presented techniques to obtain ice concentrations need improvement and future developments should involve larger datasets. However, with care, both algorithms can be used to obtain reasonable ice concentration maps with a 12.5 km times;12.5 km grid-cell size.

}, keywords = {85 GHz, Advanced Very High Resolution Radiometer data, ARTIST sea ice algorithm, ASI, AVHRR imagery, brightness temperatures, Greenland Sea, ice-type classes, oceanographic techniques, radar imaging, remote sensing by radar, SAR imagery, sea ice, SEA LION algorithm, SLA, spaceborne imagery, spaceborne radar, Special Sensor Microwave/Imager data, Spring, SSM/I ice concentration algorithms, synthetic aperture radar, texture}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2003.817181}, author = {S Kern and L Kaleschke 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 {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 {601, title = {Shape Preserving Edge Enhancement in Remote Sensing Imagery}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A novel approach to geometric shape preservation of remote sensing SAR sea ice images is presented in this paper. This approach will complement the existing edge detection schemes. The final edge is evolved by iterations. During each iteration all existing edges are checked for missing link and missing links are found by checking the orientation of the existing edges and by searching for best gradient path. When open edges are reduced considerably, opening of the features that are critical for shape preservation are found by tracing the edges and closing the boundary.

}, keywords = {best gradient path, edge detection, geometric shape preservation, geophysical signal processing, image enhancement, iteration, iterative method, measurement technique, missing link, ocean, oceanographic techniques, orientation, radar imaging, radar remote sensing, remote sensing by radar, SAR, sea ice, shape preserving edge enhancement, spaceborne radar, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026145}, author = {M S. Chowdhury and D A. Clausi} } @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 {535, title = {Motion estimation of sparse, remotely-sensed fields}, booktitle = {14th Canadian Conference on Electrical and Computer Engineering}, year = {2001}, address = {Toronto}, abstract = {

Sea surface temperature (SST) can be estimated from remotely-sensed images. Because of the sparsity of the available observation it is ideal to do estimation using dynamic methods (such as Kalman filtering). To model dynamics of SST accurately we need to know the motion of sea current. The traditional video motion estimation problem is straightforward, in some ways, because there are so few constraints. That is, the motion vectors are pretty much arbitrary, and successive image frames are densely pixellated, have the same number of pixels, with similar noise statistics. However there are many motion estimation problems, particularly in the area of remote sensing, which do not share these properties. In this paper we investigate the problem of determining the motion field of the sea surface, based on infrared measurements of surface temperature. This problem is challenging in that only a subset of the whole domain is measured at each point in time; specifically, only a few stripes are imaged. In addition, because of clouds, the measured subset varies from time to time. The quality (level of noise) can also vary from pixel to pixel. Our research is based on the following assumptions and observations: the motion field should be smooth and ideally divergence-free, i.e. the motion field is close to time-stationary. Based on these assumptions we choose to use optical flow method for this motion problem. We handle difficulty of data sparcity by pre-estimation to get a dense field. Pre-estimation can be refined by integrating this motion estimation result

}, keywords = {data sparcity, dense field, dynamic methods, geophysical signal processing, image sequences, infrared imaging, infrared measurements, Kalman filtering, motion estimation, motion field, oceanographic techniques, optical flow method, Remote Sensing, remotely-sensed images, sea current, sea surface temperature, sparse remotely-sensed fields, SST}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933601}, author = {F Jin and F M Khellah and P Fieguth and L Winger} } @article {524, title = {Multiply-rooted multiscale models for large-scale estimation}, journal = {IEEE Transactions on Image Processing}, volume = {10}, year = {2001}, pages = {1676 - 1686}, abstract = {

Divide-and-conquer or multiscale techniques have become popular for solving large statistical estimation problems. The methods rely on defining a state which conditionally decorrelates the large problem into multiple subproblems, each more straightforward than the original. However this step cannot be carried out for asymptotically large problems since the dimension of the state grows without bound, leading to problems of computational complexity and numerical stability. In this paper, we propose a new approach to hierarchical estimation in which the conditional decorrelation of arbitrarily large regions is avoided, and the problem is instead addressed piece-by-piece. The approach possesses promising attributes: it is not a local method-the estimate at every point is based on all measurements; it is numerically stable for problems of arbitrary size; and the approach retains the benefits of the multiscale framework on which it is based: a broad class of statistical models, a stochastic realization theory, an algorithm to calculate statistical likelihoods, and the ability to fuse local and nonlocal measurements

}, keywords = {asymptotically large problems, computational complexity, conditional decorrelation, estimation theory, geophysical signal processing, hierarchical estimation, image processing, large statistical estimation problems, large-scale estimation, local measurements fusion, multiply-rooted multiscale models, multiscale framework, nonlocal measurements fusion, numerical stability, oceanographic techniques, Remote Sensing, statistical analysis, statistical likelihoods, statistical models, stochastic realization theory}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/83.967396}, author = {P Fieguth} } @inproceedings {567, title = {Sea ice segmentation using Markov random fields}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Tools are required to assist the identification of pertinent classes in SAR sea ice imagery. Texture models offer a means of performing this task. The texture information in SAR sea ice imagery can be characterized by two Markov random field models: the Gauss model for conditional distribution of the observed intensity image and the discrete model for the underlying texture label image. The segmentation can be implemented as an optimization process of maximizing a posteriori distribution in a Bayesian framework

}, keywords = {Bayes methods, Bayesian framework, classes, conditional distribution, discrete model, geophysical signal processing, identification, image classification, image segmentation, image texture, intensity image, Markov processes, Markov Random Field models, maximizing a posteriori distribution, maximum likelihood estimation, oceanographic techniques, optimisation, optimization process, radar imaging, remote sensing by radar, SAR sea ice imagery, sea ice, Segmentation, synthetic aperture radar, texture models, underlying texture label image}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977102}, author = {B Yue and 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} } @inproceedings {512, title = {Large scale dynamic estimation of ocean surface temperature}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {1999}, abstract = {

This paper addresses the dynamic estimation of the ocean surface temperature based on data from the Along-Track Scanning Radiometer (ATSR) for large (512 times;512) fields. For such huge problems, the conventional dynamic estimation tool (the Kalman filter) is not directly applicable, instead, we develop a recursive estimation algorithm that emulates the Kalman filter. Our approach uses a multiscale estimation algorithm for the update step, and makes simplifying assumptions about the surface dynamics leading to a computationally efficient prediction step

}, keywords = {Along-Track Scanning Radiometer data, ATSR data, geophysical signal processing, image processing, Kalman filter, Kalman filters, large scale dynamic estimation, multiscale estimation algorithm, ocean surface temperature, oceanographic techniques, prediction step, prediction theory, radiometry, recursive estimation, recursive estimation algorithm, recursive filters, update step}, doi = {http://dx.doi.org/10.1109/IGARSS.1999.772108}, author = {P Fieguth and F M. Khellah and M J. Murray and M R. Allen} } @inproceedings {474, title = {Hierarchical methods for global-scale estimation problems}, booktitle = {IEEE Canadian Conference on Electrical and Computer Engineering}, year = {1998}, address = {Waterloo, Canada}, abstract = {

There is a substantial signal-processing challenge associated with large-scale (especially global-scale) remote-sensing problems: solving the statistical inverse problem (i.e. deducing the properties of the sensed field from measurements) by brute force, that is by covariance matrix inversion, is completely impractical for fields involving millions of pixels. This paper reports on the ongoing development of an alternative technique, in which the statistical problem is modeled on a multiscale tree, applied to estimating sea-surface temperature (SST) based on infrared radiance observations from the along-track scanning radiometers (ATSRs)

}, keywords = {along-track scanning radiometers, covariance matrix inversion, geophysical signal processing, global-scale estimation problems, hierarchical methods, image processing, infrared radiance observations, inverse problems, measurements, multiscale tree, oceanographic techniques, parameter estimation, radiometry, Remote Sensing, remote-sensing, sea-surface temperature, sensed field, signal-processing, statistical analysis, statistical inverse problem, statistical problem, tree data structures}, doi = {http://dx.doi.org/10.1109/CCECE.1998.682707}, author = {P Fieguth and M R. Allen and M J. Murray} } @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} } @inproceedings {460, title = {Multiresolution stochastic imaging of satellite oceanographic altimetric data}, booktitle = {Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference}, year = {1994}, abstract = {

Large data assimilation problems present a number of challenges, many of which the authors\&$\#$39; multiscale estimation framework is capable of addressing. They demonstrate the application of their estimation framework to an oceanographic data assimilation problem of considerable interest: the smoothing of Topex/Poseidon ocean altimetric data. they produce surface estimates and error statistics on a 512 times;512 grid in one minute on a Sparc-10. Using a generic surface reconstruction problem they demonstrate alternative uses of the multiscale framework: higher order state models, combining estimates from an ensemble of trees, and overlapping tree models

}, keywords = {error statistics, geophysical signal processing, higher order state models, image reconstruction, large data assimilation problems, multiresolution stochastic imaging, multiscale estimation framework, multiscale framework, oceanographic techniques, overlapping tree models, radar altimetry, radar imaging, remote sensing by radar, satellite oceanographic altimetric data, smoothing methods, state estimation, stochastic processes, surface estimates, surface reconstruction problem, Topex/Poseidon ocean altimetric data, trees, trees (mathematics)}, doi = {http://dx.doi.org/10.1109/ICIP.1994.413519}, author = {P Fieguth and A S. Willsky and W C. Karl} } @inproceedings {459, title = {Multiresolution stochastic imaging of satellite oceanographic altimetric data}, booktitle = {Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference}, year = {1994}, abstract = {

Large data assimilation problems present a number of challenges, many of which the authors\&$\#$39; multiscale estimation framework is capable of addressing. They demonstrate the application of their estimation framework to an oceanographic data assimilation problem of considerable interest: the smoothing of Topex/Poseidon ocean altimetric data. they produce surface estimates and error statistics on a 512 times;512 grid in one minute on a Sparc-10. Using a generic surface reconstruction problem they demonstrate alternative uses of the multiscale framework: higher order state models, combining estimates from an ensemble of trees, and overlapping tree models

}, keywords = {error statistics, geophysical signal processing, higher order state models, image reconstruction, large data assimilation problems, multiresolution stochastic imaging, multiscale estimation framework, multiscale framework, oceanographic techniques, overlapping tree models, radar altimetry, radar imaging, remote sensing by radar, satellite oceanographic altimetric data, smoothing methods, state estimation, stochastic processes, surface estimates, surface reconstruction problem, Topex/Poseidon ocean altimetric data, trees, trees (mathematics)}, doi = {http://dx.doi.org/10.1109/ICIP.1994.413519}, author = {P Fieguth and A S. Willsky and W C. Karl} } @inproceedings {461, title = {Multiresolution stochastic processing of Topex/Poseidon oceanographic altimetry}, booktitle = {IEEE OCEANS{\textquoteright}94}, year = {1994}, abstract = {

Remarkable advances in remote sensing are producing measurements on increasingly finer grids, offering the possibility of a far better understanding and characterization of the ocean surface. The sheer quantity of measurements has rendered traditional data smoothing/assimilation algorithms obsolete however, even on high performance workstations; the basis of this paper is the application of an extremely fast multiresolution smoothing framework to ocean altimetry data. Using the previously developed multiresolution framework, a simple multiscale model is developed which captures the characteristic power-law spectrum of the ocean surface. Smoothing altimetric data and computing error variances on a grid of 512 by 512 points is accomplished in about 10 seconds on a SUN Sparc-10. Finally, further applications of this framework within the oceanographic context are discussed, including model identification and anomaly detection

}, keywords = {algorithm, data assimilation, data smoothing, geophysical signal processing, geophysics computing, measurement technique, multiresolution stochastic processing, multiscale model, ocean dynamics, ocean surface, oceanographic techniques, Poseidon, radar altimetry, radar applications, radar imaging, radar signal processing, Remote Sensing, remote sensing by radar, satellite remote sensing, sea surface, signal processsing, Topex}, doi = {http://dx.doi.org/10.1109/OCEANS.1994.363913}, author = {P Fieguth and W C. Karl and A S. Willsky} }