@article {419, title = {Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {43}, year = {2005}, pages = {528 - 538}, chapter = {2940}, abstract = {

\—Environmental and sensor challenges pose difficulties for the development of computer-assisted algorithms to segment synthetic aperture radar (SAR) sea ice imagery. In this research, in support of operational activities at the Canadian Ice Service, images containing visually separable classes of either ice and water or multiple ice classes are segmented. This paper uses image intensity to discriminate ice from water and uses texture features to identify distinct ice types. In order to seamlessly combine image spatial relationships with various image features, a novel Bayesian segmentation approach is developed and applied. This new approach uses a function-based parameter to weight the two components in a Markov random field (MRF) model. The devised model allows for automatic estimation of MRF model parameters to produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to successfully segment various SAR sea ice images and achieve improvement over existing published methods including the standard MRF-based method, finite Gamma mixture model, and K-means clustering.

}, keywords = {Classification, coocurrence, expectation-maximization (EM), Gamma distribution, intensity, K-means clustering, Markov random field (MRF), mixture model, Pattern Recognition, probabilities, sea ice, Segmentation, synthetic aperture radar (SAR), texture, unsupervised}, author = {H Deng and D A. Clausi} } @inproceedings {442, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} } @inproceedings {443, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} } @inproceedings {444, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} } @inproceedings {441, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} }