@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 {387, title = {Morphological segmentation and classification of underground pipe images}, journal = {Machine Vision and Applications}, volume = {17}, year = {2006}, pages = {21 - 31}, abstract = {

Visual inspection based on closed circuit television surveys is used widely in North America to assess the condition of underground pipes. Although the human eye is extremely effective at recognition and classification, it is not suitable for assessing pipe defects in thousand of miles of pipeline because of fatigue, subjectivity, and cost. In this paper, simple, robust, and efficient image segmentation and classification algorithm for the automated analysis of scanned underground pipe images is presented. The experimental results demonstrate that the proposed algorithm can precisely segment and classify pipe cracks, holes, laterals, joints and collapse surface from underground pipe images

}, keywords = {Classification, Inspection, Morphology, Segmentation}, issn = {0932-8092}, doi = {http://dx.doi.org/10.1007/s00138-005-0012-0}, author = {S K. Sinha and P Fieguth} } @inproceedings {405, title = {Pixel-based sea ice classification using the MAGSIC system}, booktitle = {International Society for Photogrammetry and Remote Sensing}, year = {2006}, address = {Enschede, The Netherlands}, abstract = {

MAGSIC is an operation-oriented system in development dedicated to map-guided classification of sea ice for navigation route planning and meteorological modeling. It has already produced promising results in difficult situations such as the Gulf of Saint-Lawrence in late winter. The Canadian Ice Service (CIS) produces ice maps made of large regions with relatively homogeneous concentrations of different ice types. MAGSIC uses the information of these maps to produce a pixel-based (rather than region-based) ice map by labeling a Markov random field (MRF) segmentation of RADARSAT-1 data along with its derived texture features. The system uses a novel implementation of MRF segmentation in combination with a unique labeling approach based on \“cognitive reasoning\”. Although reasonably successful, the system often had difficulties identifying ice type that required cues based on the shape recognition of large ice floes or leads. This article aims at thoroughly testing the MAGSIC system using validation data acquired during the \“2003 Gulf of Saint-Lawrence SAR Validation Field Program\” performed by CIS. Some new features were also added to MAGSIC and were evaluated. Results suggest a reasonable success and that the errors can be partially attributed to the generalized nature of the analysts ́interpretation and to the difficulties of obtaining concurring ground and image data. They suggest that classification metrics that can compare sample distributions were slightly superior for labeling purposes but this could not be confirmed statistically.

}, keywords = {Classification, navigation, Radarsat, Reasoning, Segmentation, Snow Ice, texture}, author = {P Maillard 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} }