@article {10, title = {Automated ice-water classification using dual polarization SAR satellite imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, year = {2014}, abstract = {

Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) has stipulated a need for an automated ice-water discrimination algorithm using dual polarization images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. First, the HV (horizontal transmit polarization, vertical receive polarization) scene is classified using the \“glocal\” method, a hierarchical region-based classification method based on the published iterative region growing using semantics (IRGS) algorithm. Second, a pixel-based support vector machine (SVM) using a nonlinear radial basis function kernel classification is performed exploiting synthetic aperture radar grey-level co-occurrence texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 20 ground truthed dual polarization RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 96.42\% with a minimum of 89.95\% for one scene. The MAGIC system is now under consideration by CIS for operational use.

}, keywords = {Classification, grey-level co-occurerence matrix (GLCM), IRGS, RADARSAT-2, sea ice, support vector machine (SVM), synthetic aperture radar (SAR)}, author = {S Leigh and Z Wang and D A. Clausi} } @article {84, title = {SAR Image Denoising via Clustering-Based Principal Component Analysis}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {52}, year = {2014}, pages = {6858 - 6869}, chapter = {6858}, abstract = {

The combination of nonlocal grouping and transformed domain filtering has led to the state-of-the-art denoising techniques. In this paper, we extend this line of study to the denoising of synthetic aperture radar (SAR) images based on clustering the noisy image into disjoint local regions with similar spatial structure and denoising each region by the linear minimum mean-square error (LMMSE) filtering in principal component analysis (PCA) domain. Both clustering and denoising are performed on image patches. For clustering, to reduce dimensionality and resist the influence of noise, several leading principal components identified by the minimum description length criterion are used to feed the K-means clustering algorithm. For denoising, to avoid the limitations of the homomorphic approach, we build our denoising scheme on additive signal-dependent noise model and derive a PCA-based LMMSE denoising model for multiplicative noise. Denoised patches of all clusters are finally used to reconstruct the noise-free image. The experiments demonstrate that the proposed algorithm achieved better performance than the referenced state-of-the-art methods in terms of both noise reduction and image detail preservation.

}, keywords = {Clustering, denoising, linear minimum meansquare error (LMMSE), minimum description length (MDL), principal component analysis (PCA), speckle noise, synthetic aperture radar (SAR)}, author = {L Xu and J Li and Y Shu and J Peng} } @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 {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} }