@inproceedings {262, title = {Remote sensing image synthesis}, booktitle = {2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, year = {2010}, abstract = {

For remote sensing data, the testing analysis tools is difficult since the ground-truth data are not available in many cases. To address this issue, a novel method for image synthesis is presented for use as a evaluation test-bed. Given the scale-dependent, non-stationary nature of remotely sensed data, a new modeling approach that combines a resolution-oriented hierarchical method with a regional label-oriented binary tree structure is introduced to synthesize such complex data. In this paper, we are proposing on first synthesizing a label field, which contains the complex structural characteristics, then synthesizing the texture based on the generated label field for a more accurate modeling. Experimental results using operational RADARSAT SAR sea-ice image data show that the proposed method is capable of modeling complex, nonstationary scale structures, thus making it well-suitable to produce reliable, realistic remote sensing imagery.

}, keywords = {complex nonstationary scale structures, complex structural characteristics, evaluation test-bed, ground-truth data, image synthesis, label field, operational RADARSAT SAR sea-ice image data, radar imaging, regional label-oriented binary tree structure, remote sensing by radar, remote sensing data, remote sensing imagery, remotely sensed data, resolution-oriented hierarchical method, scale-dependent nonstationary nature, synthetic aperture radar, testing analysis tools}, doi = {http://dx.doi.org/10.1109/IGARSS.2010.5649342}, author = {Y Liu and A Wong and P Fieguth} } @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} } @article {590, title = {Rapid extraction of image texture by co-occurrence using a hybrid data structure}, journal = {Computers \& Geosciences}, volume = {28}, year = {2002}, pages = {763 - 774}, abstract = {

Calculation of co-occurrence probabilities is a popular method for determining texture features within remotely sensed digital imagery. Typically, the co-occurrence features are calculated by using a grey level co-occurrence matrix (GLCM) to store the co-occurring probabilities. Statistics are applied to the probabilities in the GLCM to generate the texture features. This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities when applying the statistics. 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. An improvement on the GLCLL is to utilize 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. The GLCHS method is implemented using the C language in a Unix environment. 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\% ([sigma]=3.08\%) of the computational time required by the GLCLL. Significant computational gains are made using the GLCHS method.

}, keywords = {Co-occurrence probabilities, Hash table, linked list, remote sensing imagery, texture features}, issn = {0098-3004}, doi = {http://dx.doi.org/10.1016/S0098-3004(01)00108-X}, url = {http://www.sciencedirect.com/science/article/pii/S009830040100108X}, author = {D A. Clausi and Y Zhao} }