@article {139,
	title = {Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {50},
	year = {2012},
	pages = {1302 - 1317},
	abstract = {<p>A region-based unsupervised segmentation and classification algorithm for polarimetric SAR imagery that incorporates region growing and a Markov random field (MRF) edge strength model is designed and implemented. This algorithm is an extension of the successful Iterative Region Growing with Semantics (IRGS) segmentation and classification algorithm, which was designed for amplitude only SAR imagery, to polarimetric data. Polarimetric IRGS (PolarIRGS) extends IRGS by incorporating a polarimetric feature model based on the Wishart distribution and modifying key steps such as initialization, edge strength computation and the region growing criterion. Like IRGS, PolarIRGS oversegments an image into regions and employs iterative region growing to reduce the size of the solution search space. The incorporation of an edge penalty in the spatial context model improves segmentation performance by preserving segment boundaries that traditional spatial models will smooth over. Evaluation of PolarIRGS with Flevoland fully polarimetric data shows that it improves upon two other recently published techniques in terms of classification accuracy.\&nbsp;</p>
},
	keywords = {complex, image segmentation, Markov random field (MRF), polarimetry, region adjacency graph (RAG), region-based, synthetic aperture radar (SAR), Wishart},
	author = {P Yu and D A. Clausi and K Qin}
}
@inproceedings {398,
	title = {Hierarchical region mean-based image segmentation},
	booktitle = {3rd Canadian Conference on Computer and Robot Vision},
	year = {2006},
	abstract = {<p>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</p>
},
	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}
}
