@article {319, title = {An evaluation of RADARSAT-1 and ASTER data for mapping veredas (palm swamps)}, journal = {Sensors}, year = {2008}, pages = {6055-6076}, abstract = {

Veredas (palm swamps) are wetland complexes associated with the Brazilian savanna ( cerrado ) that often represent the only available source of water for the ecosystem during the dry months. Their extent and condition a re mainly unknown and their cartography is an essential issue for their protect ion. This research article evaluates some of the fine resolution satellite data both in the rada r (Radarsat-1) and optical domain (ASTER) for the delineation and characterization of veredas . Two separate approaches are evaluated. First, given the known potential of Radarsat-1 imag es for wetland inventories, the automatic delineation of veredas is tested using only Radarsat-1 data and a Markov random fields region-based segmentation. In this case, to increase performance, processing is limited to a buffer zone around the river network. Then, characterization of their type is attempted using traditional classification methods of ASTER optical data combined with Radarsat-1 data. The automatic classification of Ra darsat data yielded results with an overall accuracy between 62 and 69\%, that proved reliable enough for delineating wide and very humid veredas . Scenes from the wet season and with a smaller ang le of incidence systematically yielded better results. For the clas sification of the main vegetation types, better results (overall success of 78.8\%) were obta ined by using only the visible and near infrared (VNIR) bands of the ASTER image. Radarsat data did not bring any improvement to these classification results. In fact, when usin g solely the Radarsat data from two different angle of incidence and two different dates, the classification results were low

}, keywords = {ASTER, Markov Random Fields, Palm swaps, Radarsat, Supervised Classification, unsupervised classification, Vegetation types, Wetlands}, author = {P Maillard and R Alencar-Silva and D A. Clausi} } @inproceedings {398, title = {Hierarchical region mean-based image segmentation}, booktitle = {3rd Canadian Conference on Computer and Robot Vision}, year = {2006}, abstract = {

Gibbs Random Fields (GRFs), which produce elegant models, but which have very poor computational speed have been widely applied to image segmentation. In contrast to block-based hierarchies usually constructed for GRFs, the irregular region-based approach is a more natural model in segmenting real images. In this paper, we show that the fineto-coarse region-based hierarchical regions framework for the well-known Potts model can be extended to non-edge based interactions. By deliberately oversegmenting at the finer scale, the method proceeds conservatively by avoiding the construction of regions which straddle a region boundary by computing region mean differences. This demonstrates the hierarchical method is able to model region interactions through new generalizations at higher levels in the hierarchy which represent regions. Promising results are presented

}, keywords = {Gibbs Random Fields, hierarchical models, image segmentation, Markov Random Fields, region-based}, isbn = {0-7695-2542-3}, doi = {http://dx.doi.org/10.1109/CRV.2006.39}, author = {S Wesolkowski and P Fieguth} } @article {438, title = {Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {42}, year = {2004}, pages = {215 - 228}, abstract = {

This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than MRFs given their respective segmentation procedures.

}, keywords = {Brodatz, discrimination ability, Feature Extraction, GLCP, gray-level cooccurrence probabilities, image segmentation, image texture, Markov processes, Markov Random Fields, MRF, Remote Sensing, remote sensing by radar, SAR sea ice imagery, sea ice, synthetic aperture radar, texture analysis, texture boundary, texture feature consistency, texture feature separability, texture methods, window size}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2003.817218}, author = {D A. Clausi and B Yue} } @inproceedings {591, title = {Adaptive color image segmentation using Markov random fields}, booktitle = {International Conference on Pattern Recognition}, year = {2002}, address = {Rochester, NY}, abstract = {

A new framework for color image segmentation is introduced generalizing the concepts of point-based and spatially-based methods. This framework is based on Markov random fields using a continuous Gibbs sampler. The Markov random fields approach allows for a rigorous computational framework where local and global spatial constraints can be globally optimized. Using a continuous Gibbs sampler enables the algorithm to adapt continuous-valued regional prototypes in a manner analogous to vector quantization while the discrete Gibbs sampler is used to adjust region boundaries.

}, keywords = {adaptive color image segmentation, adaptive signal processing, continuous Gibbs sampler, continuous-valued regional prototypes, discrete Gibbs sampler, global spatial constraints, image colour analysis, image sampling, image segmentation, local spatial constraints, Markov processes, Markov Random Fields, point-based methods, random processes, region boundaries adjustment, spatially-based methods, vector quantization}, doi = {http://dx.doi.org/10.1109/ICIP.2002.1039085}, author = {S Wesolkowski and P Fieguth} } @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} }