@article {80, title = {Despeckling of Synthetic Aperture Radar Images using Monte Carlo Texture Likelihood Sampling}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, year = {2014}, abstract = {

Speckle noise is found in synthetic aperture radar (SAR) images and can affect visualization and analysis. A novel stochastic texture-based algorithm is proposed to suppress speckle noise while preserving the underlying structural and texture detail. Based on a sorted local texture model and a FisherTippett logarithmic-space speckle distribution model, a Monte Carlo texture likelihood sampling strategy is proposed to estimate the true signal. The algorithm is compared to six other classic and state-of-the-art despeckling techniques. The comparison is performed both on synthetic noisy images added and on actual SAR images. Using peak signal-to-noise ratio, contrast-to-noise ratio and structural similarity index as image quality metrics, the proposed algorithm shows strong despeckling performance when compared to existing despeckling algorithms.

}, keywords = {Fisher-Tippett noise, noise reduction, speckle noise, synthetic aperture radar}, author = {J Glaister and A Wong and D A. Clausi} } @article {14, title = {Ice concentration estimation from dual-polarized SAR images using deep convolutional neural networks}, journal = {IEEE Transactions on Geoscience and Remote Sensing }, year = {2014}, abstract = {

High resolution ice concentration maps are of great interest for shipping and operations in the Arctic. A convolutional neural network (CNN) has been used to estimate ice concentration from C-band dual-polarized RADARSAT-2 satellite images in the melt season. SAR images are used as input and the ice concentration is the direct output from the CNN. With no feature extraction or segmentation-based post processing, the errors of the generated ice concentration maps compared to image analyses are less than 10\% on average. The CNN is demonstrated to produce ice concentration maps with more details than image analyses. Reasonable ice concentration estimations are made in the melt season, and regions of low ice concentration ice are also well
captured.

}, keywords = {convolutional neural network, Sea ice concentration, synthetic aperture radar}, author = {L Wang and A K. Scott and L Xu and D A. Clausi} } @inproceedings {110, title = {Unsupervised Classification of Sea-Ice and Water Using Synthetic Aperture Radar via an Adaptive Texture Sparsifying Transform}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2013}, abstract = {

A texture sparsifying transform for use in unsupervised classification of sea-ice in polarimetric synthetic aperture radar (SAR) imagery is presented. The goal of the sparsifying transform is to compactly represent the underlying information of the SAR imagery to eliminate sources of unwanted noise and complexities (e.g., banding effect on RADARSAT- 2) commonly found in SAR imagery. The proposed algorithm is designed to be simple to implement and discriminative in sea-ice scenes. Performing unsupervised classification on the sparsifying transform space using scenes captured with C-band HV polarization yields experimental results that are much more accurate than common pixel-based methods, and performs comparably to a recent more complex method.

}, keywords = {sea-ice classification, sparsifying transform, synthetic aperture radar, texture model}, author = {R Amelard and A Wong and D A. Clausi and F Li} } @article {179, title = {Synthesis of remote sensing label fields using a tree-structured hierarchical model}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {49}, year = {2011}, pages = {2060-2070}, abstract = {

The systematic evaluation of synthetic aperture radar (SAR) data analysis tools, such as segmentation and classification algorithms for geographic information systems, is difficult given the unavailability of ground-truth data in most cases. Therefore, testing is typically limited to small sets of pseudoground-truth data collected manually by trained experts, or primitive synthetic sets composed of simple geometries. To address this issue, we investigate the potential of employing an alternative approach, which involves the synthesis of SAR data and corresponding label fields from real SAR data for use as a reliable evaluation testbed. Given the scale-dependent nonstationary nature of SAR data, a new modeling approach that combines a resolution-oriented hierarchical method with a region-oriented binary tree structure is introduced to synthesize such complex data in a realistic manner. Experimental results using operational RADARSAT SAR sea-ice data and SIR-C/X-SAR land-mass data show that the proposed hierarchical approach can better model complex nonstationary scale structures than local MRF approaches and existing nonparametric methods, thus making it well suited for synthesizing SAR data and the corresponding label fields for potential use in the systematic evaluation of SAR data analysis tools.

}, keywords = {complex nonstationary scale structures, geographic information systems, geophysical image processing, image classification, image segmentation, Markov processes, operational RADARSAT SAR sea ice data, pseudoground truth data, region-oriented binary tree structure, remote sensing by radar, remote sensing label fields, resolution-oriented hierarchical method, SAR data analysis tools, sea ice, SIR-C/X-SAR land mass data, synthetic aperture radar, tree data structures, tree-structured hierarchical model}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2010.2102765}, author = {Y Liu and A Wong and P Fieguth} } @inproceedings {238, title = {Automated classification of operational SAR sea ice images}, booktitle = {7th Canadian Conference on Computer and Robot Vision}, year = {2010}, month = {06/2010}, address = {Ottawa, Ontario, Canada}, abstract = {

The automated classification of operational sea ice satellite imagery is important for ship navigation and environmental monitoring. Annually, thousands of large synthetic aperture radar (SAR) scenes are manually processed by the Canadian Ice Service (CIS) and pixel-level interpretation is not feasible. Trained ice analysts divide SAR images into $\#$x201D;polygon $\#$x201D; areas and then identify the number and type of ice classes per polygon. Full scene unsupervised classification can be performed by first segmenting each polygon into distinct regions algorithmically. Since there is insufficient information to assign a sea ice label for each region within an individual polygon, a Markov random field formulation using joint information to label each region in a full SAR scene has been developed. This approach has been successfully applied to operational CIS data to produce pixel-level classified images and is the first known successful end-to-end process for automatically classifying operational SAR sea ice images.

}, keywords = {automated classification, Canadian Ice Service, environmental monitoring, geophysical image processing, image segmentation, Markov processes, Markov random field, navigation, operational SAR sea ice images, pattern classification, pixel level interpretation, sea ice, sea ice satellite imagery, ship navigation, synthetic aperture radar, synthetic aperture radar scenes, unsupervised classification}, doi = {http://dx.doi.org/10.1109/CRV.2010.59}, author = {S Ochilov and D A. Clausi} } @article {224, title = {MAGIC: MAp-Guided Ice Classification system for operational analysis}, journal = {Canadian Journal of Remote Sensing}, volume = {36}, year = {2010}, pages = {S13 - S25}, abstract = {

A map-guided ice classification (MAGIC) system that aims at effectively interpreting SAR sea ice images using the associated ice charts in an operational environment is presented. As an ongoing project, MAGIC version 1.0 has been developed using operational SAR image data from the Canadian ice service (CIS). MAGIC is intended to not only be used for SAR sea ice classification research and development, but also used for classification research of generic digital imagery using the available fundamental and advanced algorithms. At some point, we hope to make the system freely available.

}, keywords = {Canadian Ice Service, image classification, MAGIC, map-guided ice classification, operational analysis, radar imaging, SAR sea ice images, sea ice, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/PRRS.2008.4783172}, author = {D A. Clausi and K Qin and M S. Chowdhury and P Yu and P Maillard} } @inproceedings {262, title = {Remote sensing image synthesis}, booktitle = {2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, year = {2010}, abstract = {

For remote sensing data, the testing analysis tools is difficult since the ground-truth data are not available in many cases. To address this issue, a novel method for image synthesis is presented for use as a evaluation test-bed. Given the scale-dependent, non-stationary nature of remotely sensed data, a new modeling approach that combines a resolution-oriented hierarchical method with a regional label-oriented binary tree structure is introduced to synthesize such complex data. In this paper, we are proposing on first synthesizing a label field, which contains the complex structural characteristics, then synthesizing the texture based on the generated label field for a more accurate modeling. Experimental results using operational RADARSAT SAR sea-ice image data show that the proposed method is capable of modeling complex, nonstationary scale structures, thus making it well-suitable to produce reliable, realistic remote sensing imagery.

}, keywords = {complex nonstationary scale structures, complex structural characteristics, evaluation test-bed, ground-truth data, image synthesis, label field, operational RADARSAT SAR sea-ice image data, radar imaging, regional label-oriented binary tree structure, remote sensing by radar, remote sensing data, remote sensing imagery, remotely sensed data, resolution-oriented hierarchical method, scale-dependent nonstationary nature, synthetic aperture radar, testing analysis tools}, doi = {http://dx.doi.org/10.1109/IGARSS.2010.5649342}, author = {Y Liu and A Wong and P Fieguth} } @inproceedings {287, title = {IceSynth: An image synthesis system for sea-ice segmentation evaluation}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

An ongoing challenge in automatic sea-ice monitoring using synthetic aperture radar (SAR) is the automatic segmentation of SAR sea-ice images based on the underlying ice type. Given the intractability of obtaining ground-truth segmentation data from polar regions, the evaluation of automatic SAR sea-ice image segmentation algorithms is generally limited to tests using real SAR imagery based on pseudo-ground truth data (e.g., manual segmentations) and simple synthetic tests using basic shape primitives. As such, it is difficult to evaluate automatic segmentation algorithms in a systematic and reliable manner using realistic scenarios. To tackle this issue, a novel image synthesis system named IceSynth is presented, which is capable of generating a variety of synthetic sea-ice images that are representative of real SAR sea-ice imagery. In IceSynth, SAR sea-ice textures for each ice type are synthesized via stochastic sampling based on non-parametric local conditional texture probability distribution estimates. A stochastic sampling approach based on non-parametric local class probability distribution estimates is used to generate large-scale sea-ice structures of various ice types based on ice classification priors extracted from real SAR sea-ice imagery. Experimental results show that IceSynth is capable of generating realistic-looking SAR sea-ice images that are well-suited for performing objective evaluation of SAR sea-ice image segmentation algorithms.

}, keywords = {automatic sea-ice monitoring, ice classification, ice types, IceSynth, image classification, image segmentation, image synthesis system, image texture, oceanographic techniques, polar regions, probability, probability distribution estimation, pseudo-ground truth data, remote sensing by radar, SAR imagery, sea ice, sea-ice segmentation evaluation, sea-ice structures, sea-ice textures, stochastic processes, stochastic sampling, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/CRV.2009.27}, author = {A Wong and W Zhang and D A. Clausi} } @inproceedings {283, title = {SAR sea ice image segmentation using an edge-preserving region-based MRF}, booktitle = {16th IEEE International Conference on Image Processing (ICIP)}, year = {2009}, month = {11/2009}, abstract = {

In this paper, we propose a novel edge-preserving region (EPR)-based representation for synthetic aperture radar (SAR) images, which is incorporated with a region-level Markov random field (MRF) model to offer an efficient approach to the segmentation of SAR sea ice images. The EPR-based representations of SAR images are constructed by applying the speckle reduction anisotropic diffusion (SRAD) algorithm and the watershed transform, which aims at suppressing oversegmentation within objects while accurately locating object edges at region boundaries in the presence of speckle noise. In combination with a region-level MRF, the EPR-based representation largely reduces the search space of optimization process and improves parameter estimation of feature model, leading to considerable computational savings and less probability of false segmentation. Relative to the existing region-level MRF-based methods, testing results have demonstrated that the proposed method achieves more than 50\% reduction of computational time and improves the segmentation accuracy especially at high speckle noise.

}, keywords = {edge-preserving region, image segmentation, Markov processes, MRF model, parameter estimation, region-level Markov random field, SAR sea ice image segmentation, sea ice, speckle noise, speckle reduction anisotropic diffusion, SRAD algorithm, synthetic aperture radar, synthetic aperture radar images, watershed transform}, doi = {http://dx.doi.org/10.1109/ICIP.2009.5414606}, author = {X Yang and D A. Clausi} } @inproceedings {290, title = {SEC: Stochastic ensemble consensus approach to unsupervised SAR sea-ice segmentation}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

The use of synthetic aperture radar (SAR) has become an integral part of sea-ice monitoring and analysis in the polar regions. An important task in sea-ice analysis is to segment SAR sea-ice imagery based on the underlying ice type, which is a challenging task to perform automatically due to various imaging and environmental conditions. A novel stochastic ensemble consensus approach to sea-ice segmentation (SEC) is presented to tackle this challenging task. In SEC, each pixel in the SAR sea-ice image is assigned an initial sub-class based on its tonal characteristics. Ensembles of random samples are generated from a random field representing the SAR sea-ice imagery. The generated ensembles are then used to re-estimate the sub-class of the pixels using a weighted median consensus strategy. Based on the probability distribution of the sub-classes, an expectation maximization (EM) approach is utilized to estimate the final class likelihoods using a Gaussian mixture model (GMM). Finally, maximum likelihood (ML) classification is performed to estimate the final class of each pixel within the SAR sea-ice imagery based on the estimated GMM and the assigned sub-classes. SEC was tested using a variety of operational RADARSAT-1 and RADARSAT-2 SAR sea-ice imagery provided by the Canadian Ice Service (CIS) and was shown to produce successfully segmentation results that were superior to approaches based on K-means clustering, Gamma mixture models, and Markov Random Field (MRF) models for sea-ice segmentation.

}, keywords = {Canadian Ice Service, expectation maximization approach, Gaussian mixture model, GMM, image segmentation, K-means clustering, Markov Random Field models, maximum likelihood classification, maximum likelihood estimation, oceanographic techniques, polar regions, probability, probability distribution, RADARSAT-1 imagery, RADARSAT-2 imagery, random samples, remote sensing by radar, SAR sea-ice imagery, sea ice, sea-ice analysis, sea-ice monitoring, stochastic ensemble consensus approach, synthetic aperture radar, tonal characteristics, underlying ice type, unsupervised SAR sea-ice segmentation, weighted median consensus strategy}, doi = {http://dx.doi.org/10.1109/CRV.2009.25}, author = {A Wong and D A. Clausi and P Fieguth} } @inproceedings {284, title = {Structure-preserving speckle reduction of SAR images using nonlocal means filters}, booktitle = {16th IEEE International Conference on Image Processing (ICIP)}, year = {2009}, month = {11/2009}, address = {Cairo, Egypt}, abstract = {

This paper proposes a structure-preserving speckle reduction (SPSR) algorithm for synthetic aperture radar (SAR) images by exploiting self-similarity of structural patterns based on nonlocal means filter. The SPSR algorithm is featured by discerning pixels of similar structural patterns, which is crucial for a despeckling process to avoid blurring image structure. To alleviate the impact of speckle noise to similarity measure, a two-stage filtering scheme is introduced into the SPSR algorithm. Filtering at the first stage aims at an accurate approximation of true structural similarity, followed by the filtering at the second stage to group pixels with similar neighborhood in a large area. Compared to the traditional Lee filter, enhanced Lee filter and the speckle reducing anisotropic diffusion (SRAD), evaluation results have shown that the SPSR algorithm substantially improves the despeckling performance especially on structure preservation and speckle reduction in homogeneous regions.

}, keywords = {enhanced Lee filter, filtering theory, Image Denoising, nonlocal means filters, radar imaging, SAR images, speckle reducing anisotropic diffusion, structure-preserving speckle reduction, synthetic aperture radar, synthetic aperture radar images, two-stage filtering scheme}, doi = {http://dx.doi.org/10.1109/ICIP.2009.5414502}, author = {X Yang and D A. Clausi} } @inproceedings {327, title = {Automatic registration of inter-band and inter-sensor images using robust complex wavelet feature representations}, booktitle = {5th Workshop on Pattern Recognition for Remote Sensing}, year = {2008}, month = {12/2008}, address = {Tampa, Florida, USA}, abstract = {

A robust method for registering inter-band and inter-sensor remote sensing images has been designed and implemented. The proposed method introduces noise-resilient and contrast invariant control point detection and control point matching schemes based on robust complex wavelet feature representations. Furthermore, an iterative refinement scheme is introduced for achieving improved control point pair localization and mapping function estimation between the images being registered. The registration accuracy of the proposed method was demonstrated on the registration of multi-spectral optical and synthetic aperture radar (SAR) images. The proposed method achieves better registration accuracy when compared with the state-of-the-art MSSD and ARRSI registration algorithms.

}, keywords = {ARRSI, automatic registration, contrast invariant control point detection, control point matching schemes, control point pair localization, estimation theory, Feature Extraction, image matching, image registration, image representation, images registration, inter-band images, inter-sensor images, iterative methods, iterative refinement scheme, mapping function estimation, MSSD, multispectral optical images, registration algorithms, Remote Sensing, remote sensing images, robust complex wavelet feature representations, synthetic aperture radar, synthetic aperture radar images, wavelet transforms}, doi = {http://dx.doi.org/10.1109/PRRS.2008.4783164}, author = {A Wong and D A. Clausi and P Fieguth} } @inproceedings {362, title = {SAR sea ice image segmentation based on edge-preserving watersheds}, booktitle = {4th Annual Canadian Conference on Computer and Robot Vision}, year = {2007}, month = {05/2007}, address = {Montreal, Quebec, Canada}, abstract = {

This paper presents a hybrid method for the segmentation of SAR sea ice images, which consists of an initial watershed segmentation followed by a region merging. Iterative bilateral filtering is used to reduce speckle noise and suppress irrelevant image details, which can significantly alleviate oversegmentation of watersheds. Since edges are well preserved by bilateral filtering, the watershed algorithm is capable of precisely locating object boundaries. Final segmentation is accomplished by applying an iterative region merging on the watershed regions by taking into account local boundary strengths and regional statistics. The efficiency of the proposed method has been demonstrated on the segmentation of SAR sea ice images. In comparison with traditional watershed algorithm, our method achieves better performance in identifying filament structures such as leads.

}, keywords = {edge-preserving watersheds, filtering theory, image segmentation, iterative bilateral filtering, iterative methods, local boundary strengths, radar imaging, region merging, regional statistics, SAR sea ice image segmentation, speckle noise, statistical analysis, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/CRV.2007.58}, author = {X Yang and D A. Clausi} } @article {357, title = {SAR sea-ice image analysis based on iterative region growing using semantics}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {45}, year = {2007}, pages = {3919 - 3931}, abstract = {

Synthetic aperture radar (SAR) has been intensively used for sea-ice monitoring in polar regions. A computer-assisted analysis of SAR sea-ice imagery is extremely difficult due to numerous imaging parameters and environmental factors. This paper presents a system which, with some limited information provided, is able to perform an automated segmentation and classification for the SAR sea-ice imagery. In the system, both the segmentation and classification processes are based on a Markov random-field theory and are formulated in a joint manner under the Bayesian framework. Solutions to the formulation are obtained by a region-growing technique which keeps refining the segmentation and producing semantic class labels at the same time in an iterative manner. The algorithm is a general-segmentation approach named iterative region growing using semantics, which, in this paper, is dedicated to the problem of classifying the operational SAR sea-ice imagery provided by the Canadian Ice Service (CIS). The classified image results have been validated by the CIS personnel, and the resulting classifications are quite successful using the same algorithm applied to diverse data sets.

}, keywords = {automated segmentation, Bayes methods, Bayesian framework, Canadian Ice Service, iterative region growing technique, Markov random-field theory, oceanographic techniques, polar regions, remote sensing by radar, sea ice, sea-ice image analysis, sea-ice monitoring, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/TGRS.2007.908876}, author = {Q Yu and D A. Clausi} } @article {383, title = {Filament preserving model (FPM) segmentation applied to SAR sea-ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {44}, year = {2006}, pages = {3687 - 3694}, abstract = {

Modeling spatial context constraints using a Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to synthetic aperture radar (SAR) sea-ice segmentation has also been demonstrated recently. However, most existing MRF models are not capable of preserving filaments, specifically leads and ridges for SAR sea ice, which are valuable for ship navigation applications and necessary for identifying certain ice types. In this paper, a new statistical context model is proposed that, within the same scene, can simultaneously preserve narrow elongated features while producing similar smooth segmentation results comparable to typical MRF-based approaches. Tested on one synthetic image and two SAR sea-ice scenes, this filament preserving model substantially improves classification accuracies when compared to standard Gaussian mixture and MRF-based segmentation algorithms

}, keywords = {egg code, filament preserving model, image classification, image segmentation, Markov processes, Markov random field, MRF-based segmentation algorithm, noisy image segmentation, oceanographic techniques, remote sensing by radar, SAR sea-ice imagery, sea ice, ship navigation, standard Gaussian mixture, synthetic aperture radar, synthetic image}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2006.885046}, author = {Q Yu and D A. Clausi} } @inproceedings {393, title = {Filament preserving segmentation for SAR sea ice imagery using a new statistical model}, booktitle = {18th International Conference on Pattern Recognition (ICPR)}, year = {2006}, month = {08/2006}, address = {Hong Kong}, abstract = {

Modelling spatial context constraints using Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to SAR sea ice segmentation has also been demonstrated by Deng and Clausi (2005). However, most existing MRF models are not capable of preserving filaments, specifically leads and ridges for SAR sea ice, which are valuable for ship navigation applications and helpful for identifying certain ice types. A new statistical context model is proposed that can preserve such narrow elongated features while producing similar smooth segmentation results as those of existing MRF based approaches

}, keywords = {Feature Extraction, filament preserving segmentation, geophysical signal processing, Image Denoising, image segmentation, Markov processes, Markov random field, narrow elongated features, noisy image segmentation, oceanographic techniques, radar imaging, SAR sea ice imagery, sea ice, ship navigation, spatial context constraints, statistical analysis, statistical model, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/ICPR.2006.561}, author = {Q Yu and D A. Clausi} } @inproceedings {394, title = {Joint image segmentation and interpretation using iterative semantic region growing on SAR sea ice imagery}, booktitle = {18th International Conference on Pattern Recognition (ICPR)}, year = {2006}, month = {08/2006}, address = {Hong Kong}, abstract = {

Segmentation of images into disjoint regions and interpretation of the regions for semantic meanings are two central tasks in an image analysis system. Typically, the segmentation and interpretation are performed separately with the interpretation as a post processing of segmentation. In this paper, we use an iterative method that keeps refining the segmentation and producing semantic class labels at the same time. The segmentation algorithm is based on a region growing technique and the interpretation is a Markov random field (MRF) based classification. The two processes are integrated under the Bayesian framework, with both aiming at reducing a defined energy. The interactions between the two are bidirectional by letting the interpretation result have some degree of control on the region growing process. Various features can hence be efficiently combined, and accurate classifications are obtained for operational synthetic aperture radar (SAR) sea ice applications

}, keywords = {Bayes methods, Bayesian framework, image analysis, image classification, image interpretation, image segmentation, iterative methods, iterative semantic region growing, Markov processes, Markov random field based classification, radar imaging, SAR sea ice imagery, sea ice, semantic class labels, synthetic aperture radar, terrain mapping}, doi = {http://dx.doi.org/10.1109/ICPR.2006.734}, author = {Q Yu and D A. Clausi} } @article {389, title = {Preserving boundaries for image texture segmentation using grey level co-occurring probabilities}, journal = {Pattern Recognition}, volume = {39}, year = {2006}, pages = {234 - 235}, abstract = {

Texture analysis has been used extensively in the computer-assisted interpretation of digital imagery. A popular texture feature extraction approach is the grey level co-occurrence probability (GLCP) method. Most investigations consider the use of the GLCP texture features for classification purposes only, and do not address segmentation performance. Specifically, for segmentation, the pixels in an image located near texture boundaries have a tendency to be misclassified. Boundary preservation when using the GLCP texture features for image segmentation is important. An advancement which exploits spatial relationships has been implemented. The generated features are referred to as weighted GLCP (WGLCP) texture features. In addition, an investigation for selecting suitable GLCP parameters for improved boundary preservation is presented. From the tests, WGLCP features provide improved boundary preservation and segmentation accuracy at a computational cost. As well, the GLCP correlation statistical parameter should not be used when segmenting images with high contrast texture boundaries.

}, keywords = {Co-occurence probabilities, Co-occurrence matrix, Computer Vision, Digital imaging, Remote Sensing, sea ice, Segmentation, synthetic aperture radar, texture}, issn = {0031-3203}, doi = {http://dx.doi.org/10.1016/j.patcog.2005.07.010}, url = {http://www.sciencedirect.com/science/article/pii/S0031320305002992}, author = {R Jobanputra and D A. Clausi} } @article {417, title = {Operational map-guided classification of SAR sea ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {43}, year = {2005}, pages = {2940 - 2951}, abstract = {

This paper presents a map-guided sea ice classification system built to work in parallel with the Canadian Ice Service (CIS) operations to produce pixel-based ice maps that complement actual \"egg code\" maps produced by CIS. The system uses the CIS maps as input to guide classification by providing information on the number of ice types and their final label for specific regions. Segmentation is based on a modified adaptive Markov random field (MRF) model that uses synthetic aperture radar (SAR) intensities and texture features as input. The ice type labeling is performed automatically by gathering evidences based on a priori information on one or two classes and deducing the other labels iteratively by comparing distributions of segments. Three methods for comparing the segment distributions (Fisher criterion, Mahalanobis distance, and Kolmogorov-Smirnov test) were implemented and compared. The system is fully described with special attention to the labeling procedure. Examples are presented in the form of two CIS SAR-based ice maps from the Gulf of Saint Lawrence region and one example from the Beaufort Sea. The results indicate that when the segmentation is good, the labeling attains best results (between 71\% and 89\%) based on evaluation by a sea ice analyst. Some problems remain to be assessed which are primarily attributable to discrepancies in the information provided by the egg code and what is actually visible in the SAR image. Subscale information on floe size and shape available to human analysts, but not in this classification system, also appear to be a critical information for separating some ice types.

}, keywords = {Beaufort Sea, Canadian Ice Service, Fisher criterion, geophysical signal processing, gray-level cooccurrence matrix, Gulf of Saint Lawrence, image classification, image segmentation, image texture, Kolmogorov-Smirnov test, Mahalanobis distance, map-guided classification, Markov processes, Markov random field, oceanographic regions, oceanographic techniques, pixel-based ice maps, radar imaging, remote sensing by laser beam, remote sensing by radar, SAR sea ice imagery, sea ice, sea ice classification, segment distribution comparison, synthetic aperture radar}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2005.857897}, author = {P Maillard and D A. Clausi and H Deng} } @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 {694, title = {Advanced Gaussian MRF rotation-invariant texture features for classification of remote sensing imagery}, booktitle = {2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition}, year = {2003}, address = {Madison, WI, USA}, abstract = {

The features based on Markov random field (MRF) models are usually sensitive to the rotation of image textures. The paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for modeling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate (LSE) method, an approximate least squares estimate (ALSE) method is proposed to estimate the parameters of ACGMRF model. The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional (1D) discrete Fourier transform (DFT). Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR (synthetic aperture radar) sea ice and Brodatz imagery.

}, keywords = {ACGMRF model, ALSE method, anisotropic circular Gaussian MRF, approximate least squares estimate, Brodatz imagery, discrete Fourier transform, discrete Fourier transforms, Feature Extraction, Gaussian distribution, image classification, image texture rotation, least squares approximation, least squares approximations, Markov processes, Markov random field, one-dimensional DFT, Remote Sensing, remote sensing image classification, rotated image texture modeling, rotation-invariant feature, rotation-invariant texture feature, SAR classification, sea ice, singularity problem, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/CVPR.2003.1211533}, author = {H Deng and D A. Clausi} } @article {813, title = {A comparison of two 85-GHz SSM/I ice concentration algorithms with AVHRR and ERS-2 SAR imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {41}, year = {2003}, pages = {2294 - 2306}, abstract = {

Sea ice concentrations obtained with two algorithms from Special Sensor Microwave/Imager (SSM/I) data are compared to spaceborne visible/infrared and active microwave imagery for the Greenland Sea in spring. Both algorithms, the ARTIST Sea Ice algorithm (ASI) and the SEA LION algorithm (SLA), utilize 85-GHz SSM/I brightness temperatures with a spatial resolution of 15 km times;13 km. Ice concentrations obtained from Advanced Very High Resolution Radiometer (AVHRR) infrared data in cloud-free areas are underestimated by SLA and ASI ice concentrations by 3.6\% and 8.3\% (correlation coefficients of 0.90 and 0.91). Ice concentrations estimated from texture classified ERS-2 synthetic aperture radar (SAR) images by assigning experience-based ice concentrations to ice-type classes are overestimated by SLA and ASI ice concentrations by 4.4\% and 1.5\% (correlation coefficients of 0.84 and 0.77). However, omitting low/high ice concentrations forming up to 80\% (AVHRR) and 60\% (SAR) of the entire dataset reveals a significantly different statistic. For instance, the correlation between AVHRR and SLA and ASI ice concentrations drops to 0.77 and 0.70, respectively. All presented techniques to obtain ice concentrations need improvement and future developments should involve larger datasets. However, with care, both algorithms can be used to obtain reasonable ice concentration maps with a 12.5 km times;12.5 km grid-cell size.

}, keywords = {85 GHz, Advanced Very High Resolution Radiometer data, ARTIST sea ice algorithm, ASI, AVHRR imagery, brightness temperatures, Greenland Sea, ice-type classes, oceanographic techniques, radar imaging, remote sensing by radar, SAR imagery, sea ice, SEA LION algorithm, SLA, spaceborne imagery, spaceborne radar, Special Sensor Microwave/Imager data, Spring, SSM/I ice concentration algorithms, synthetic aperture radar, texture}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2003.817181}, author = {S Kern and L Kaleschke and D A. Clausi} } @inproceedings {649, title = {Operational segmentation and classification of SAR sea ice imagery}, booktitle = {2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, An Honorary Workshop for Prof. David A. Landgrebe}, year = {2003}, abstract = {

The Canadian Ice Service (CIS) is a government agency responsible for monitoring ice-infested regions in Canada\&$\#$39;s jurisdiction. Synthetic aperture radar (SAR) is the primary tool used for monitoring such vast, inaccessible regions. Ice maps of different regions are generated each day in support of navigation operations and environmental assessments. Currently, operators digitally segment the SAR data manually using primarily tone and texture visual characteristics. Regions containing multiple ice types are identified, however, it is not feasible to produce a pixel-based segmentation due to time constraints. In this research, advanced methods for performing texture feature extraction, incorporating tonal features, and performing the segmentation are presented. Examples of the segmentation of a SAR image that is difficult to segment manually and that requires the inclusion of both tone and texture features are presented.

}, keywords = {Canada, Canadian Ice Service, environmental assessments, Feature Extraction, geophysical signal processing, government agency, ice maps, image classification, image segmentation, image texture, navigation operations, oceanographic regions, operational segmentation, pixel based segmentation, radar imaging, SAR image, SAR sea ice imagery, sea ice, synthetic aperture radar, texture visual characteristics, tone visual characteristics}, doi = {http://dx.doi.org/10.1109/WARSD.2003.1295204}, author = {D A. Clausi and H Deng} } @inproceedings {593, title = {Automatic registration of SAR and visible band remote sensing images}, booktitle = {Joint IEEE International Geoscience and Remote Sensing Symposium and 24th Canadian Remote Sensing Symposium}, year = {2002}, abstract = {

Image registration is one of the basic image processing operations in remote sensing. With an increasing number of images collected every day from different sensors, automated registration of multi-sensor/multi-spectral images has become an important issue. A wide range of registration techniques exists for different types of applications and data sources, however no algorithm is known that can accurately register multi-source images consistently. This research addresses this problem by investigating the development of a fully automatic registration system for synthetic aperture radar (SAR) and optical remote sensing images. The development of this new automatic image registration method is based on the extraction and matching of common features that are visible in both images. The algorithm involves the following five steps: noise removal, edge extraction, edge linking pattern extraction and pattern matching. The application of the developed automatic image registration model to SAR and optical image pairs showed that accurate ground control points (GCPs) could be identified automatically.

}, keywords = {algorithm, edge extraction, edge linking pattern extraction, fully automatic registration, geophysical measurement technique, geophysical signal processing, geophysical techniques, image registration, land surface, multi-source image, noise removal, optical imaging, pattern matching, radar imaging, radar remote sensing, Remote Sensing, remote sensing by radar, remote sensing image, SAR, sensor fusion, synthetic aperture radar, terrain mapping, visible band}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026106}, author = {M Ali and D A. Clausi} } @article {586, title = {Hierarchical network flow phase unwrapping}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {40}, year = {2002}, pages = {1695 - 1708}, abstract = {

The well-studied interferometric synthetic aperture radar (InSAR) problem for digital elevation map generation involves the derivation of topography from the radar phase. The topography is a function of the full phase, whereas the measured phase is known modulo 2 pi;, necessitating the process of recovering full phase values via phase unwrapping. This mathematical process becomes difficult through the presence of noise and phase discontinuities. This paper is motivated by research which models phase unwrapping as a network-flow minimization problem. A major limitation is that often a substantial computational effort is required to find solutions. Commonly, these phase images are huge ( Gt;10 million pixels), and obviously the sheer size of the problem itself makes phase unwrapping challenging. This paper addresses the development of a computationally efficient hierarchical algorithm, based on a \"divide-and-conquer\" approach. We have shown that the phase-unwrapping problem can first be partitioned into independent phase-unwrapping subproblems, which can further be recombined to produce the unwrapped phase. Interestingly, the recombination step itself can be interpreted as an unwrapping problem, for which a modified network-flow solution applies! In short, this paper develops a parallelization of the network-flow algorithm, allowing images of virtually unlimited size to be unwrapped and leading to dramatic decreases in the algorithm execution time.

}, keywords = {computationally efficient hierarchical algorithm, digital elevation map generation, divide-and-conquer approach, full phase, geophysical signal processing, hierarchical network flow phase unwrapping, independent phase-unwrapping subproblems, InSAR problem, interferometric synthetic aperture radar problem, measured phase, modified network-flow solution, network-flow algorithm, network-flow minimization problem, parallelization, phase images, radar imaging, radiowave interferometry, recombination step, remote sensing by radar, SAR, synthetic aperture radar, topography, topography (Earth)}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2002.800279}, author = {G F. Carballo and P Fieguth} } @inproceedings {601, title = {Shape Preserving Edge Enhancement in Remote Sensing Imagery}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A novel approach to geometric shape preservation of remote sensing SAR sea ice images is presented in this paper. This approach will complement the existing edge detection schemes. The final edge is evolved by iterations. During each iteration all existing edges are checked for missing link and missing links are found by checking the orientation of the existing edges and by searching for best gradient path. When open edges are reduced considerably, opening of the features that are critical for shape preservation are found by tracing the edges and closing the boundary.

}, keywords = {best gradient path, edge detection, geometric shape preservation, geophysical signal processing, image enhancement, iteration, iterative method, measurement technique, missing link, ocean, oceanographic techniques, orientation, radar imaging, radar remote sensing, remote sensing by radar, SAR, sea ice, shape preserving edge enhancement, spaceborne radar, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026145}, author = {M S. Chowdhury and D A. Clausi} } @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} } @inproceedings {567, title = {Sea ice segmentation using Markov random fields}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Tools are required to assist the identification of pertinent classes in SAR sea ice imagery. Texture models offer a means of performing this task. The texture information in SAR sea ice imagery can be characterized by two Markov random field models: the Gauss model for conditional distribution of the observed intensity image and the discrete model for the underlying texture label image. The segmentation can be implemented as an optimization process of maximizing a posteriori distribution in a Bayesian framework

}, keywords = {Bayes methods, Bayesian framework, classes, conditional distribution, discrete model, geophysical signal processing, identification, image classification, image segmentation, image texture, intensity image, Markov processes, Markov Random Field models, maximizing a posteriori distribution, maximum likelihood estimation, oceanographic techniques, optimisation, optimization process, radar imaging, remote sensing by radar, SAR sea ice imagery, sea ice, Segmentation, synthetic aperture radar, texture models, underlying texture label image}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977102}, author = {B Yue and D A. Clausi} } @article {575, title = {The effect of speckle filtering on scale-dependent texture estimation of a forested scene}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {38}, year = {2000}, pages = {1160 - 1170}, abstract = {

Spatial fluctuations in microwave backscatter may be an important piece of information in discriminating tree stands. However, the presence of speckle in synthetic aperture radar (SAR) image data is a barrier to the exploitation of image texture. The authors explored a new methodology that combines a recent adaptive speckle reduction algorithm by Lopes et al. (1990) with a generic texture estimation scheme. They investigated the claim that this filter was capable of preserving backscatter texture. To understand if speckle reduction was destroying backscatter texture, they compared the strength of the relationship between forest inventory parameters and image texture as a function of spatial scale for both filtered and unfiltered images. They used Radarsat Fine mode image data: single look resolution is approximately 8.5 m, and pixel spacing is 3 m. Their study area was northern Vancouver Island, B.C., on the west coast of Canada. For the unfiltered data, they found that the ability of image texture to predict the forest parameters decreased as the texture scale increased from 3 to 13 m, suggesting greater information content in the small scale texture. For the filtered data, this relationship was much weaker at small scales and was not a function of distance. Their results suggest that the speckle filter was not retaining small scale texture, which is consistent with the theoretical hypotheses underlying its multiplicative noise model. They also show that there is significant information in small state SAR image texture that may be used as an adjunct to other spatial information for discriminating tree stands in the temperate rain forest

}, keywords = {adaptive signal processing, adaptive speckle reduction algorithm, backscatter texture, forest, forested scene, forestry, generic texture estimation scheme, geophysical measurement technique, geophysical signal processing, geophysical techniques, image texture, land surface, microwave backscatter, radar imaging, radar remote sensing, remote sensing by radar, SAR, scale-dependent texture estimation, spatial fluctuations, speckle, speckle filtering, synthetic aperture radar, tree stand, vegetation mapping}, issn = {0196-2892}, author = {M J. Collins and J Wiebe and D A. Clausi} } @inproceedings {503, title = {Multiresolution Network Flow Phase Unwrapping}, booktitle = {IEEE 2000 International Geoscience and Remote Sensing Symposium}, year = {2000}, abstract = {

Phase unwrapping is a key step in deriving topographic information from interferometric SAR. Several recent methods are based on a minimum cost network flow approach. The authors develop a new, computationally efficient, ldquo;divide-and-conquer rdquo; strategy for phase unwrapping

}, keywords = {divide and conquer methods, divide-and-conquer method, geophysical measurement technique, geophysical techniques, InSAR, interferometric SAR, land surface topography, multiresolution network flow phase unwrapping, phase unwrapping, radar remote sensing, radar theory, remote sensing by radar, synthetic aperture radar, terrain mapping, topography (Earth)}, doi = {http://dx.doi.org/10.1109/IGARSS.2000.861693}, author = {G F Carballo and P Fieguth} } @inproceedings {513, title = {Probabilistic cost functions for network flow phase unwrapping}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {1999}, abstract = {

Phase unwrapping is a key step in deriving topographic information from interferometric SAR. A recent method is based on minimizing the total weighted length of branch cuts by computing the minimum cost flow on a network. The authors provide a theoretical foundation to derive the network weights based on a probabilistic approach

}, keywords = {branch cuts, geophysical measurement technique, geophysical techniques, InSAR, interferometric SAR, land surface topography, minimizing, minimum cost flow, network flow phase unwrapping, network weights, probabilistic approach, probabilistic cost function, radar imaging, radar remote sensing, radar theory, remote sensing by radar, SAR, synthetic aperture radar, terrain mapping, theoretical foundation, topographic mapping, topography (Earth), total weighted length}, doi = {http://dx.doi.org/10.1109/IGARSS.1999.772009}, author = {G F Carballo and P Fieguth} }