3D Computation of Gray Level Co-occurrence in Hyperspectral Image Cubes

This study extended the computation of GLCM (gray level co-occurrence matrix) to a three-dimensional form. The objective was to treat hyperspectral image cubes as volumetric data sets and use the developed 3D GLCM computation algorithm to extract discriminant volumetric texture features for classification. As the kernel size of the moving box is the most important factor for the computation of GLCM-based texture descriptors, a three-dimensional semi-variance analysis algorithm was also developed to determine appropriate moving box sizes for 3D computation of GLCM from different data sets. The developed algorithms were applied to a series of classifications of two remote sensing hyperspectral image cubes and comparing their performance with conventional GLCM textural classifications. Evaluations of the classification results indicated that the developed semi-variance analysis was effective in determining the best kernel size for computing GLCM. It was also demonstrated that textures derived from 3D computation of GLCM produced better classification results than 2D textures.

[1]  Fritz Albregtsen,et al.  Adaptive gray level run length features from class distance matrices , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[2]  Fuan Tsai,et al.  Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species , 2007 .

[3]  Bertrand Zavidovique,et al.  Content based image retrieval using motif cooccurrence matrix , 2004, Image Vis. Comput..

[4]  Kee Tung. Wong,et al.  Texture features for image classification and retrieval. , 2002 .

[5]  Richard C. Dubes,et al.  Performance evaluation for four classes of textural features , 1992, Pattern Recognit..

[6]  Andrea Baraldi,et al.  An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Grégoire Toussaint,et al.  Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. , 2003, Magnetic resonance imaging.

[8]  David A. Clausi,et al.  Preserving boundaries for image texture segmentation using grey level co-occurring probabilities , 2006, Pattern Recognit..

[9]  Dengliang Gao,et al.  Volume texture extraction for 3D seismic visualization and interpretation , 2003 .

[10]  David A Clausi.  An analysis of co-occurrence texture statistics as a function of grey level quantization , 2002 .

[11]  James M. Keller,et al.  Texture description and segmentation through fractal geometry , 1989, Comput. Vis. Graph. Image Process..

[12]  Fuan Tsai,et al.  Derivative Analysis of Hyperspectral Data , 1998 .

[13]  R. Edelman,et al.  Magnetic resonance imaging (2) , 1993, The New England journal of medicine.

[14]  Tim R. McVicar,et al.  Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes , 2003, IEEE Trans. Geosci. Remote. Sens..

[15]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  S.B. Serpico,et al.  Classification of optical high resolution images in urban environment using spectral and textural information , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[17]  F. Parmiggiani,et al.  An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Fritz Albregtsen,et al.  Low dimensional adaptive texture feature vectors from class distance and class difference matrices , 2004, IEEE Transactions on Medical Imaging.

[19]  Fuan Tsai,et al.  Texture augmented analysis of high resolution satellite imagery in detecting invasive plant species , 2006 .

[20]  William Philpot,et al.  A derivative-aided hyperspectral image analysis system for land-cover classification , 2002, IEEE Trans. Geosci. Remote. Sens..

[21]  Constantino Carlos Reyes-Aldasoro,et al.  Volumetric Texture Description and Discriminant Feature Selection for MRI , 2003, EUROCAST.

[22]  Assia Kourgli,et al.  Texture primitives description and segmentation using variography and mathematical morphology , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[23]  J. M. Hans du Buf,et al.  A review of recent texture segmentation and feature extraction techniques , 1993 .

[24]  Michael Spann,et al.  Texture feature performance for image segmentation , 1990, Pattern Recognit..

[25]  Stefano Bistarelli,et al.  Representing Biological Systems with Multiset Rewriting , 2003 .

[26]  Yoshitomo Yaginuma,et al.  Classification of solid textures using 3D mask patterns , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[27]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..