@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} } @inproceedings {649, title = {Operational segmentation and classification of SAR sea ice imagery}, booktitle = {2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, An Honorary Workshop for Prof. David A. Landgrebe}, year = {2003}, abstract = {

The Canadian Ice Service (CIS) is a government agency responsible for monitoring ice-infested regions in Canada\&$\#$39;s jurisdiction. Synthetic aperture radar (SAR) is the primary tool used for monitoring such vast, inaccessible regions. Ice maps of different regions are generated each day in support of navigation operations and environmental assessments. Currently, operators digitally segment the SAR data manually using primarily tone and texture visual characteristics. Regions containing multiple ice types are identified, however, it is not feasible to produce a pixel-based segmentation due to time constraints. In this research, advanced methods for performing texture feature extraction, incorporating tonal features, and performing the segmentation are presented. Examples of the segmentation of a SAR image that is difficult to segment manually and that requires the inclusion of both tone and texture features are presented.

}, keywords = {Canada, Canadian Ice Service, environmental assessments, Feature Extraction, geophysical signal processing, government agency, ice maps, image classification, image segmentation, image texture, navigation operations, oceanographic regions, operational segmentation, pixel based segmentation, radar imaging, SAR image, SAR sea ice imagery, sea ice, synthetic aperture radar, texture visual characteristics, tone visual characteristics}, doi = {http://dx.doi.org/10.1109/WARSD.2003.1295204}, author = {D A. Clausi and H Deng} } @inproceedings {449, title = {An advanced computational method to determine co-occurrence probability texture features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A critical shortcoming of determining co-occurrence probability texture features using Haralick\&$\#$39;s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04\% to 16\%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.

}, keywords = {algorithm, co-occurrence probability, co-occurrence probability texture features, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, GLCHH, GLCHS, GLCIA, grey level co occurrence hybrid histogram, grey level co occurrence hybrid structure, grey level co occurrence integrated algorithm, image texture, land surface, Remote Sensing, terrain mapping, texture feature}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026575}, author = {D A. Clausi and Y Zhao} } @inproceedings {592, title = {An advanced computational method to determine co-occurrence probability texture features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A critical shortcoming of determining co-occurrence probability texture features using Haralick\&$\#$39;s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04\% to 16\%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.

}, keywords = {algorithm, co-occurrence probability, co-occurrence probability texture features, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, GLCHH, GLCHS, GLCIA, grey level co occurrence hybrid histogram, grey level co occurrence hybrid structure, grey level co occurrence integrated algorithm, image texture, land surface, Remote Sensing, terrain mapping, texture feature}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026575}, author = {D A. Clausi and Y Zhao} } @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 {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 {586, title = {Hierarchical network flow phase unwrapping}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {40}, year = {2002}, pages = {1695 - 1708}, abstract = {

The well-studied interferometric synthetic aperture radar (InSAR) problem for digital elevation map generation involves the derivation of topography from the radar phase. The topography is a function of the full phase, whereas the measured phase is known modulo 2 pi;, necessitating the process of recovering full phase values via phase unwrapping. This mathematical process becomes difficult through the presence of noise and phase discontinuities. This paper is motivated by research which models phase unwrapping as a network-flow minimization problem. A major limitation is that often a substantial computational effort is required to find solutions. Commonly, these phase images are huge ( Gt;10 million pixels), and obviously the sheer size of the problem itself makes phase unwrapping challenging. This paper addresses the development of a computationally efficient hierarchical algorithm, based on a \"divide-and-conquer\" approach. We have shown that the phase-unwrapping problem can first be partitioned into independent phase-unwrapping subproblems, which can further be recombined to produce the unwrapped phase. Interestingly, the recombination step itself can be interpreted as an unwrapping problem, for which a modified network-flow solution applies! In short, this paper develops a parallelization of the network-flow algorithm, allowing images of virtually unlimited size to be unwrapped and leading to dramatic decreases in the algorithm execution time.

}, keywords = {computationally efficient hierarchical algorithm, digital elevation map generation, divide-and-conquer approach, full phase, geophysical signal processing, hierarchical network flow phase unwrapping, independent phase-unwrapping subproblems, InSAR problem, interferometric synthetic aperture radar problem, measured phase, modified network-flow solution, network-flow algorithm, network-flow minimization problem, parallelization, phase images, radar imaging, radiowave interferometry, recombination step, remote sensing by radar, SAR, synthetic aperture radar, topography, topography (Earth)}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2002.800279}, author = {G F. Carballo 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 {528, title = {Design and establishment of multi-scale spatial information system based on regional planning and decision making}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Fast development of Geomatics and network integrated technology demonstrates that spatial information are widely used in regional planning and decision making. Existing non-standardized multi-sources and multi-scales data have a shortage of spatial information sharing in both internal and external application departments or units, especially in the network era. A pilot regional planning and decision making spatial information system based on a standardized and network sharing technology is presented in this paper. The research area is composed of eight counties in Jinghong Region and Simao Region of Yunnan Province in China. Spatial information resources such as remote sensing, digital maps, and paper maps are collected from different departments. Statistics data such as agriculture, forest, earthquake hazard, ore, hydrogeology, ecology, transportation, population, tourism, and trade, etc. are the composition of the application system. The system will be mainly used by regional planners and managers in the province and the regions via Internet. The applications of general users are also considered. Geographic Metadata standardization for spatial information is used as an Internet engine between users and datasets. Arc/INFO and ArcView GIS are the basic software for information integration. WebGIS is used for spatial information sharing design and GeoMedia is accepted as a basic web software platform. Other software for multi-dimensional information display and management are designed and applied for virtual reality planning and application in the project

}, keywords = {agriculture, China, decision making, decision support systems, forest, geographic information system, geographic information systems, geomatics, geophysical measurement technique, geophysical signal processing, geophysical techniques, geophysics computing, GIS, Jinghong, land surface, land use, metadata, multi-dimensional information display, multiscale spatial information system, population, regional planning, Remote Sensing, sharing technology, Simao, tourism, town and country planning, vegetation, WebGIS, Yunnan Province}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977131}, author = {Y Zhao and 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 {566, title = {Rapid determination of co-occurrence texture features}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Typically, the co-occurrence features for image processing are calculated-by using a grey level co-occurrence matrix (GLCM). This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. This paper presents a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4\% of the computational time ( sigma;=3.08\%) required by the GLCLL. Significant computational gains are made using the GLCHS method

}, keywords = {Brodatz test image, co-occurrence features, co-occurrence texture feature, geophysical measurement technique, geophysical signal processing, geophysical techniques, grey level co-occurrence hybrid structure, grey level co-occurrence linked list, grey level co-occurrence matrix, image processing, image texture, integrated hash table, land surface, linked list, Remote Sensing, terrain mapping}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977103}, author = {D A. Clausi and Y Zhao} } @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 {448, title = {Using the Canny edge detector for feature extraction and enhancement of remote sensing images}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Edges are important features in an image since they represent significant local intensity changes. They provide important clues to separate regions within an object or to identify changes in illumination. Most remote sensing applications, such as image registration, image segmentation, region separation, object description, and recognition, use edge detection as a preprocessing stage for feature extraction. Real images, such as remote sensing images, can be corrupted with point noise. The real problem is how to enhance noisy remote sensing images and simultaneously extract the edges. Using the implemented Canny edge detector for feature extraction and as an enhancement tool for remote sensing images, the result was robust with a very high enhancement level

}, keywords = {Canny edge detector, edge detection, enhancement, Feature Extraction, geophysical signal processing, illumination, image enhancement, local intensity changes, noise, point noise, preprocessing stage, real images, remote sensing images, terrain mapping}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977981}, author = {M Ali 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} } @article {575, title = {The effect of speckle filtering on scale-dependent texture estimation of a forested scene}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {38}, year = {2000}, pages = {1160 - 1170}, abstract = {

Spatial fluctuations in microwave backscatter may be an important piece of information in discriminating tree stands. However, the presence of speckle in synthetic aperture radar (SAR) image data is a barrier to the exploitation of image texture. The authors explored a new methodology that combines a recent adaptive speckle reduction algorithm by Lopes et al. (1990) with a generic texture estimation scheme. They investigated the claim that this filter was capable of preserving backscatter texture. To understand if speckle reduction was destroying backscatter texture, they compared the strength of the relationship between forest inventory parameters and image texture as a function of spatial scale for both filtered and unfiltered images. They used Radarsat Fine mode image data: single look resolution is approximately 8.5 m, and pixel spacing is 3 m. Their study area was northern Vancouver Island, B.C., on the west coast of Canada. For the unfiltered data, they found that the ability of image texture to predict the forest parameters decreased as the texture scale increased from 3 to 13 m, suggesting greater information content in the small scale texture. For the filtered data, this relationship was much weaker at small scales and was not a function of distance. Their results suggest that the speckle filter was not retaining small scale texture, which is consistent with the theoretical hypotheses underlying its multiplicative noise model. They also show that there is significant information in small state SAR image texture that may be used as an adjunct to other spatial information for discriminating tree stands in the temperate rain forest

}, keywords = {adaptive signal processing, adaptive speckle reduction algorithm, backscatter texture, forest, forested scene, forestry, generic texture estimation scheme, geophysical measurement technique, geophysical signal processing, geophysical techniques, image texture, land surface, microwave backscatter, radar imaging, radar remote sensing, remote sensing by radar, SAR, scale-dependent texture estimation, spatial fluctuations, speckle, speckle filtering, synthetic aperture radar, tree stand, vegetation mapping}, issn = {0196-2892}, author = {M J. Collins and J Wiebe and D A. Clausi} } @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} } @article {471, title = {A fast method to determine co-occurrence texture features}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {36}, year = {1998}, pages = {298 - 300}, abstract = {

A critical shortcoming of determining texture features derived from grey-level co-occurrence matrices (GLCM\&$\#$39;s) is the excessive computational burden. This paper describes the implementation of a linked-list algorithm to determine co-occurrence texture features far more efficiently. Behavior of common co-occurrence texture features across difference grey-level quantizations is investigated

}, keywords = {co-occurrence texture feature, fast method, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, grey-level co-occurrence matrices, grey-level quantization, image feature, image processing, image texture, land surface, linked-list algorithm, occurrence matrix, Remote Sensing, terrain mapping}, doi = {http://dx.doi.org/10.1109/36.655338}, author = {D A. Clausi and E Jernigan} } @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} }