@inproceedings {207, title = {Satellite Orbit Determination using Ground-based Navigation Data}, booktitle = {European Navigation Conference}, year = {2011}, publisher = {ENC 2011}, organization = {ENC 2011}, address = {London, UK}, abstract = {

There exist various methods of using ground signals collected by a satellite to determine the orbit of the satellite. These signals can be used independently as a single source, or multi-source data can be fused together to increase redundancy and reliability of the orbit solution. A new measurement technique is proposed in this study, in which the location of a satellite could be computed from the knowledge of navigation data points within the satellite\’s field of view (FoV). There are a growing number of ground-based sensors which broadcast their position, and which can be detected from space, providing a dense dataset for determining the position of the detecting satellite. Knowing the positions of such points, the time that they appear and remain in the FoV, it is possible to determine the satellite\’s orbit.

In the context of this problem, it is proposed to use ship-based Automatic Identification System (AIS) data as the ground points whose positions are known (with some uncertainty) through the AIS reports from the ships. This AIS navigation data, which are broadcast by the ships and can be acquired by the satellite, can be fed into an orbit determination tool to fit a trajectory for the satellite. A number of simplifying assumptions will be made initially to formulate the approach for deriving the algorithms required to use such measurements. Thereafter, the assumptions will be able to be relaxed successively to provide an analysis technique robust enough to handle real-world data.

}, keywords = {Machine vision, Orbit, Satellite, ship navigation}, author = {R Yousuf and F Newland and T Kirubarajan} } @inproceedings {238, title = {Automated classification of operational SAR sea ice images}, booktitle = {7th Canadian Conference on Computer and Robot Vision}, year = {2010}, month = {06/2010}, address = {Ottawa, Ontario, Canada}, abstract = {

The automated classification of operational sea ice satellite imagery is important for ship navigation and environmental monitoring. Annually, thousands of large synthetic aperture radar (SAR) scenes are manually processed by the Canadian Ice Service (CIS) and pixel-level interpretation is not feasible. Trained ice analysts divide SAR images into $\#$x201D;polygon $\#$x201D; areas and then identify the number and type of ice classes per polygon. Full scene unsupervised classification can be performed by first segmenting each polygon into distinct regions algorithmically. Since there is insufficient information to assign a sea ice label for each region within an individual polygon, a Markov random field formulation using joint information to label each region in a full SAR scene has been developed. This approach has been successfully applied to operational CIS data to produce pixel-level classified images and is the first known successful end-to-end process for automatically classifying operational SAR sea ice images.

}, keywords = {automated classification, Canadian Ice Service, environmental monitoring, geophysical image processing, image segmentation, Markov processes, Markov random field, navigation, operational SAR sea ice images, pattern classification, pixel level interpretation, sea ice, sea ice satellite imagery, ship navigation, synthetic aperture radar, synthetic aperture radar scenes, unsupervised classification}, doi = {http://dx.doi.org/10.1109/CRV.2010.59}, author = {S Ochilov 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} }