@inproceedings {263, title = {Texture classification using compressed sensing}, booktitle = {This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing and bag of words model, suitable for large texture database applications with images obtained under unknown viewpoint and illumination. }, year = {2010}, abstract = {

This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing and bag of words model, suitable for large texture database applications with images obtained under unknown viewpoint and illumination. At the feature extraction stage, a small set of random features are extracted from local image patches. The random features are embedded into the bag of words model to perform texture classification. Random feature extraction surpasses many conventional feature extraction methods, despite their careful design and complexity. We conduct extensive experiments on the CUReT database to evaluate the performance of the proposed approach. It is demonstrated that excellent performance can be achieved by the proposed approach using a small number of random features, as long as the dimension of the feature space is above certain threshold. Our approach is compared with recent state-of-the-art methods: the Patch method (Varma and Zisserman, TPAMI 09), the MR8 filter bank method (Varma and Zisserman, IJCV 05) and the LBP method (Ojala et al., TPAMI 02). It is shown that the proposed method significantly outperforms MR8 and LBP and is at least as good as the Patch method with drastic reduction in storage and computational complexity.

}, keywords = {bag of words model, band-pass filters, Compressed Sensing, computational complexity, Feature Extraction, image classification, image texture, LBP method, MR8 filter bank method, patch method, Texture classification, texture database applications}, doi = {http://dx.doi.org/10.1109/CRV.2010.16}, author = {L Liu 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 {366, title = {A medical texture local binary pattern for TRUS prostate segmentation}, booktitle = {29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2007}, abstract = {

Prostate cancer diagnosis and treatment rely on segmentation of Transrectal Ultrasound (TRUS) prostate images. This is a challenging and difficult task dut to weak prostate boundaries, speckle noise and the short range of gray levels. Advances in digital imaging techniques have made it possible the acquisition of large volumes of TRUS prostate images so that there is considerable demand for automated segmentation systems. Local binary pattern (LBP) has been used for texture segmentation and analysis. Despite its promising performance for texture classification it has not yet been considered for TRUS prostate segmentation. In this paper we introduce a medical texture local binary pattern operator designed for applications of medical imaging where different tissues or micro organisms might maintain extremely weak underlying textures that make it impossible or very difficult for ordinary texture analysis approaches to classify them. In the proposed method the deformations of a level set contour are controlled based on the medical texture local binary pattern operator.

}, keywords = {automated segmentation systems, biomedical ultrasonics, cancer, digital imaging techniques, image classification, image segmentation, image texture, LBP, level set contour deformations, medical image processing, medical texture local binary pattern, prostate cancer diagnosis, prostate cancer treatment, short gray level range, speckle noise, texture analysis, Texture classification, texture segmentation, transrectal ultrasound prostate image segmentation, TRUS prostate segmentation}, doi = {http://dx.doi.org/10.1109/IEMBS.2007.4353617}, author = {N Nezamoddin Kachouie and P Fieguth} } @inproceedings {750, title = {A hybrid algorithm using discrete cosine transform and Gabor filter bank}, booktitle = {17th Canadian Conference on Electrical and Computer Engineering}, year = {2006}, abstract = {

Gabor filters have been widely used for texture segmentation and feature extraction, however there are important considerations regarding filter parameters, filter bank coverage in the frequency domain and feature dimensional reduction. In this paper, a texture segmentation algorithm based on a hybrid filter bank is presented. The proposed method uses a Gabor filter bank and discrete cosine transform (GDCT) to extract the optimal features for texture segmentation. To reduce the feature vector dimension a competitive network is trained to estimate the principal components of the extracted features. The feature vectors composing both Gabor and DCT features are quantized by estimated eigenvectors. The proposed method enables the use of multiple filter banks or larger filter banks consisting of a higher number of channels.

}, keywords = {channel bank filters, competitive network training, discrete cosine transform, discrete cosine transforms, eigenvalues and eigenfunctions, eigenvector quantization, feature dimensional reduction, Feature Extraction, filter bank coverage, filter parameters, Gabor filter bank, GDCT, hybrid algorithm, hybrid filter bank, image segmentation, image texture, multiple filter banks, neural nets, neural networks, optimal features, principal component analysis, principal components estimation, texture segmentation, unsupervised learning}, doi = {http://dx.doi.org/10.1109/CCECE.2004.1349767}, author = {N Nezamoddin Kachouie and J Alirezaie and P Fieguth} } @article {415, title = {Design-based texture feature fusion using Gabor filters and co-occurrence probabilities}, volume = {14}, year = {2005}, pages = {925 - 936}, abstract = {

A design-based method to fuse Gabor filter and grey level co-occurrence probability (GLCP) features for improved texture recognition is presented. The fused feature set utilizes both the Gabor filter\&$\#$39;s capability of accurately capturing lower and mid-frequency texture information and the GLCP\&$\#$39;s capability in texture information relevant to higher frequency components. Evaluation methods include comparing feature space separability and comparing image segmentation classification rates. The fused feature sets are demonstrated to produce higher feature space separations, as well as higher segmentation accuracies relative to the individual feature sets. Fused feature sets also outperform individual feature sets for noisy images, across different noise magnitudes. The curse of dimensionality is demonstrated not to affect segmentation using the proposed the 48-dimensional fused feature set. Gabor magnitude responses produce higher segmentation accuracies than linearly normalized Gabor magnitude responses. Feature reduction using principal component analysis is acceptable for maintaining the segmentation performance, but feature reduction using the feature contrast method dramatically reduced the segmentation accuracy. Overall, the designed fused feature set is advocated as a means for improving texture segmentation performance.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, design-based texture feature fusion, feature contrast, feature reduction, Gabor filter, grey level cooccurrence probability, image classification, image enhancement, image interpretation, image segmentation, image segmentation classification, image texture, Information Storage and Retrieval, Models, Pattern Recognition, principal component analysis, probability, Statistical, Subtraction Technique, texture recognition}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2005.849319}, author = {D A. Clausi and H Deng} } @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 {629, title = {Feature fusion for image texture segmentation}, booktitle = {17th International Conference on Pattern Recognition (ICPR)}, year = {2004}, abstract = {

A design-based method to fuse Gabor filter and grey level co-occurrence probability (GLCP) features for improved texture recognition is presented. Feature space separability and unsupervised image segmentation are used for testing. The fused features are robust with respect to the curse of dimensionality and additive noise. Feature reduction methods are typically detrimental to the segmentation performance. Overall, the fused features are a definite improvement over non-fused features and are advocated in texture analysis applications.

}, keywords = {additive noise, design based method, Feature Extraction, feature fusion, feature reduction method, Gabor filter, grey level cooccurrence probability, image segmentation, image texture, image texture segmentation, probability, texture analysis, texture recognition, unsupervised image segmentation}, doi = {http://dx.doi.org/10.1109/ICPR.2004.1334207}, author = {D A. Clausi and H Deng} } @article {690, title = {Gaussian MRF rotation-invariant features for image classification}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {26}, year = {2004}, pages = {951 - 955}, abstract = {

Features based on Markov random field (MRF) models are sensitive to texture rotation. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate method, an approximate least squares estimate method is designed and implemented. Rotation-invariant features are obtained from the ACGMRF model parameters using the discrete Fourier transform. The ACGMRF model is demonstrated to be a statistical improvement over three published methods. The three methods include a Laplacian pyramid, an isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features.

}, keywords = {Algorithms, anisotropic circular Gaussian MRF model, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, discrete Fourier transform, discrete Fourier transforms, Gaussian MRF rotation-invariant features, gray level cooccurrence probability features, image classification, image enhancement, image interpretation, image texture, Laplacian pyramid, Markov Chains, Markov processes, Markov Random Field models, Models, Normal Distribution, Pattern Recognition, Rotation, rotation-invariant texture features, Statistical, statistical improvement, texture rotation}, issn = {0162-8828}, doi = {http://dx.doi.org/10.1109/TPAMI.2004.30}, author = {H Deng and D A. Clausi} } @inproceedings {627, title = {Texture segmentation comparison using grey level co-occurrence probabilities and Markov random fields}, booktitle = {17th International Conference on Pattern Recognition (ICPR)}, year = {2004}, month = {08/2004}, address = {Cambridge, United Kingdom}, abstract = {

The discrimination ability of texture features derived from Gaussian Markov random fields (GMRFs) and grey level co-occurrence probabilities (GLCPs) are compared and contrasted. More specifically, the role of window size in feature consistency and separability as well as the role of multiple textures within a window are investigated. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, an important concept when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than GMRFs.

}, keywords = {discrimination ability, Feature Extraction, Gaussian Markov random fields, Gaussian processes, grey level co-occurrence probabilities, image segmentation, image texture, Markov processes, probability, texture features, texture segmentation}, doi = {http://dx.doi.org/10.1109/ICPR.2004.1334208}, author = {D A. Clausi and B Yue} } @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 {449, title = {An advanced computational method to determine co-occurrence probability texture features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A critical shortcoming of determining co-occurrence probability texture features using Haralick\&$\#$39;s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04\% to 16\%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.

}, keywords = {algorithm, co-occurrence probability, co-occurrence probability texture features, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, GLCHH, GLCHS, GLCIA, grey level co occurrence hybrid histogram, grey level co occurrence hybrid structure, grey level co occurrence integrated algorithm, image texture, land surface, Remote Sensing, terrain mapping, texture feature}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026575}, author = {D A. Clausi and Y Zhao} } @inproceedings {592, title = {An advanced computational method to determine co-occurrence probability texture features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A critical shortcoming of determining co-occurrence probability texture features using Haralick\&$\#$39;s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04\% to 16\%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.

}, keywords = {algorithm, co-occurrence probability, co-occurrence probability texture features, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, GLCHH, GLCHS, GLCIA, grey level co occurrence hybrid histogram, grey level co occurrence hybrid structure, grey level co occurrence integrated algorithm, image texture, land surface, Remote Sensing, terrain mapping, texture feature}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026575}, author = {D A. Clausi and Y Zhao} } @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 {566, title = {Rapid determination of co-occurrence texture features}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Typically, the co-occurrence features for image processing are calculated-by using a grey level co-occurrence matrix (GLCM). This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. This paper presents a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4\% of the computational time ( sigma;=3.08\%) required by the GLCLL. Significant computational gains are made using the GLCHS method

}, keywords = {Brodatz test image, co-occurrence features, co-occurrence texture feature, geophysical measurement technique, geophysical signal processing, geophysical techniques, grey level co-occurrence hybrid structure, grey level co-occurrence linked list, grey level co-occurrence matrix, image processing, image texture, integrated hash table, land surface, linked list, Remote Sensing, terrain mapping}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977103}, author = {D A. Clausi and Y Zhao} } @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} } @article {471, title = {A fast method to determine co-occurrence texture features}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {36}, year = {1998}, pages = {298 - 300}, abstract = {

A critical shortcoming of determining texture features derived from grey-level co-occurrence matrices (GLCM\&$\#$39;s) is the excessive computational burden. This paper describes the implementation of a linked-list algorithm to determine co-occurrence texture features far more efficiently. Behavior of common co-occurrence texture features across difference grey-level quantizations is investigated

}, keywords = {co-occurrence texture feature, fast method, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, grey-level co-occurrence matrices, grey-level quantization, image feature, image processing, image texture, land surface, linked-list algorithm, occurrence matrix, Remote Sensing, terrain mapping}, doi = {http://dx.doi.org/10.1109/36.655338}, author = {D A. Clausi and E Jernigan} } @inproceedings {475, title = {Multipole-motivated reduced-state estimation}, booktitle = {International Conference on Image Processing}, year = {1998}, address = {Chicago}, abstract = {

This paper discusses efficient solutions to large-scale two-dimensional estimation problems, using reduced state methods motivated by the multipole method of mathematical physics. The work is mainly exploratory, building on past efforts in multiscale statistical signal modeling and estimation. We will illustrate applications to the estimation of Markov random field textures, with the motivation and goal the estimation of remotely-sensed fields

}, keywords = {image texture, large-scale 2D estimation problems, Markov processes, Markov random field textures, mathematical physics, multipole method, multipole-motivated reduced-state estimation, multiscale statistical signal estimation, multiscale statistical signal modeling, parameter estimation, reduced state methods, Remote Sensing, remotely-sensed fields, state estimation, statistical analysis}, doi = {http://dx.doi.org/10.1109/ICIP.1998.723580}, author = {P Fieguth} }