@article {77, title = {Bayesian classification of hyperspectral imagery based on probabilistic sparse representation and Markov random field}, journal = {IEEE Geoscience and Remote Sensing Letters}, volume = {11}, year = {2014}, pages = {823 - 827}, chapter = {823}, abstract = {

This letter presents a Bayesian method for hyperspectral image classification based on the sparse representation (SR) of spectral information and the Markov random field modeling of spatial information. We introduce a probabilistic SR approach to estimate the class conditional distribution, which proved to be a powerful feature extraction technique to be combined with the label prior distribution in a Bayesian framework. The resulting maximum a priori problem is estimated by a graph-cut-based α-expansion technique. The capabilities of the proposed method are proven in several benchmark hyperspectral images of both agricultural and urban areas.

}, keywords = {Bayes classifier, graph cut, hyperspectral image classification, Markov random field (MRF), probabilistic sparse representation (PSR)}, author = {L Xu and J Li} } @article {78, title = {BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification}, journal = {IEEE Transactions on Image Processing}, volume = {23}, year = {2014}, abstract = {

In this paper we propose a simple, efficient, yet robust multi-resolution approach to texture classification \— Binary Rotation Invariant and Noise Tolerant (BRINT). The proposed approach is very fast to build, very compact while remaining robust to illumination variations, rotation changes and noise.
We develop a novel and simple strategy \— averaging before binarization \— to compute a local binary descriptor based on the conventional LBP approach, preserving the advantageous characteristics of uniform LBP. Points are sampled in a circular neighborhood, but keeping the number of bins in a single-scale LBP histogram constant and small, such that arbitrarily large circular neighborhoods can be sampled and compactly encoded over a number of scales. There is no need for a pre-training step at the feature extraction stage, no necessity to learn a texton dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. Extensive experimental results on several popular benchmark texture databases demonstrate that the proposed approach is very robust to noise and significantly outperforms the state-of-the-art in terms of classifying noise corrupted textures, while at the same time the proposed approach still improves the classification performance on the original texture databases, producing consistently good classification results, including what we believe to be the best reported results for CUReT and the three standard Outex test suites.

}, keywords = {Bayes classifier, graph cut, hyperspectral image classification, Markov random field (MRF), probabilistic sparse representation (PSR)}, author = {L Liu and Y Long and P Fieguth and S Lao and G Zhao} } @article {128, title = {Evaluating SAR sea ice image segmentation using edge-preserving region-based MRFs.}, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)}, volume = {5}, year = {2012}, pages = {1383-1393}, abstract = {

This paper presents a new approach to sea ice segmentation in synthetic aperture radar (SAR) intensity images by combining an edge-preserving region (EPR)-based representation with region-level MRF models. To construct the EPR-based representation of a SAR image, edge strength is measured using instantaneous coefficient of variation (ICOV) upon which the watershed algorithm is applied to partition the image into primitive regions. In addition, two new metrics for quantitative assessment of region characteristics (region accuracy and region redundancy) are defined and used for parameter estimation in the ICOV extraction process towards desired region characteristics. In combination with a region-level MRF, the EPR-based representation facilitates the segmentation process by largely reducing the search space of optimization process and improving parameter estimation of feature model, leading to considerable computational savings and less probability of false segmentation. The proposed segmentation method has been evaluated using a synthetic sea ice image corrupted with varying levels of speckle noise as well as real SAR sea ice images. Relative to the existing region-level MRF-based methods, testing results have demonstrated that our proposed method substantially improves the segmentation accuracy at high speckle noise and achieves on average 29\% reduction of computational time.

}, keywords = {instantaneous coefficient of variation, Markov random field (MRF), sea ice classification, synthetic aperture radar (SAR), watersheds}, author = {X Yang and D A. Clausi} } @article {125, title = {Unsupervised operational SAR sea ice image classification}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {50}, year = {2012}, pages = {4397-4408}, abstract = {

Thousands of spaceborne synthetic aperture radar (SAR) sea-ice images are systematically processed every year in support of operational activities such as ship navigation and environmental monitoring. An automated approach that generates a pixel-level sea-ice image classification is required since manual pixel-level classification is not feasible. Currently, using a standardized approach, trained ice analysts manually segment full SAR scenes into smaller polygons to record ice types and concentrations. Using this data, pixel-level classification can be achieved by initial unsupervised segmentation of each polygon followed by automatic sea-ice labeling of the full scene. A fully automated Markov random field model that is used to assign labels to all segmented regions in the full scene has been designed and implemented. This approach is the first known successful end-to-end process for operational SAR sea-ice image classification. In addition, a novel performance evaluation framework has been developed to validate the segmentation and labeling of SAR sea-ice images. A trained sea-ice expert has conducted an arms length evaluation using this framework to generate a set of full scene reference images used for testing. Testing demonstrates operational success of the labeling approach.

}, keywords = {image classification, Markov random field (MRF), sea-ice, Synthetic, unsupervised segmentation}, author = {S Ochilov and D A. Clausi} } @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 = {

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.\ 

}, 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} } @article {222, title = {Icesynth II: Synthesis of SAR sea-ice imagery using region-based posterior sampling}, journal = {IEEE Geosciences and Remote Sensing Letters}, volume = {7}, year = {2010}, pages = {348 - 351}, abstract = {

A novel method for synthesizing synthetic aperture radar (SAR) sea-ice imagery named IceSynth II is presented. A Markov random field model is assumed, and a conditional sampling approach is used to learn local conditional posterior probability distributions on a regional basis. Synthetic SAR sea-ice images and the associated ground-truth segmentations are generated using a region-based posterior sampling approach. Experimental results using single-polarization RADARSAT-1 and dual-polarization RADARSAT-2 SAR sea-ice imagery provided by the Canadian Ice Service show that IceSynth II is capable of producing SAR sea-ice imagery that is more realistic than existing approaches. The synthesized images are well suited for performing systematic and reliable objective evaluation of SAR sea-ice image segmentation methods.

}, keywords = {Conditional, Markov random field (MRF), multivariate region growing, posterior, sampling ice, synthesis, synthetic aperture radar (SAR)}, author = {A Wong and P Yu and W Zhang and D A. Clausi} } @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} } @article {621, title = {Unsupervised image segmentation using a simple MRF model with a new implementation scheme}, journal = {Pattern Recognition}, volume = {37}, year = {2004}, pages = {2323{\textendash}2335}, abstract = {

A simple Markov random field model with a new implementation scheme is proposed for unsupervised image segmentation based on image features. The traditional two-component MRF model for segmentation requires training data to estimate necessary model parameters and is thus unsuitable for unsupervised segmentation. The new implementation scheme solves this problem by introducing a function-based weighting parameter between the two components. Using this method, the simple MRF model is able to automatically estimate model parameters and produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to segment various types of images (gray scale, color, texture)and achieves an improvement over the traditional method.

}, keywords = {Color Image, Expectation-maximization (EM) algorithm, Feature space, image segmentation, K-means clustering, Markov random field (MRF), sea ice, synthetic aperture radar (SAR), Texture Image, unsupervised segmentation}, author = {H Deng and D A. Clausi} } @inproceedings {445, title = {Unsupervised segmentation of synthetic aperture radar sea ice imagery using MRF models}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, month = {05/2004}, address = {London, Ontario, Canada}, abstract = {

Due to both environmental and sensor reasons, it is challenging to develop computer-assisted algorithms to segment SAR (synthetic aperture radar) sea ice imagery. In this research, images containing either ice and water or multiple ice classes are segmented. This paper proposes to use the image intensity to discriminate ice from water and to use texture features to separate different ice types. In order to seamlessly combine spatial relationship information in an ice image with various image features, a novel Bayesian segmentation approach is developed Experiments demonstrate that the proposed algorithm is able to segment both types of sea ice images and achieves an improvement over the standard MRF (Markov random field) based method, the finite Gamma mixture model and the K-means clustering method.

}, keywords = {expectation-maximization (EM), Gamma distribution, image feature, image segmentation, K-means clustering, Markov random field (MRF), mixture model, sea ice, synthetic aperture radar (SAR), texture, unsupervised segmentation}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301420}, author = {H Deng and D A. Clausi} }