Cover page and Table of Contents: PDF (size: 1399KB)
Full Text (PDF, 1399KB), PP.1-10
Views: 0 Downloads: 0
Texture analysis, clustering, Bayesian inference, remote sensing
Texture is one of the most significant characteristics for retrieving visually similar patterns in remote sensing images. Traditional approaches for texture analysis are based on symbolic descriptions and statistical methods. This study proposes a new method to extract and classify texture patterns from multispectral Landsat TM satellite images using optimized clustering and probabilistic inference. After the images are preprocessed with Principal Component Analysis and decomposed into regions of interest, Gabor wavelets are computed for each region in the first component image to obtain texture feature vectors. An adapted k-means clustering algorithm with optimized number of clusters and initial starting centers generates training and testing data for Bayes Point Machine classifiers. The classifiers may run in the online mode for binary classification and the batch mode for multi-class classification. The experimental results show the effectiveness of the proposed classification method and its potentials in other image texture pattern recognition applications.
Jiang Li, William Rich, Donald Buhl-Brown,"Texture Analysis of Remote Sensing Imagery with Clustering and Bayesian Inference", IJIGSP, vol.7, no.9, pp.1-10, 2015. DOI: 10.5815/ijigsp.2015.09.01
S. W. Myint, "A robust texture analysis and classification approach for urban land-use and land-cover feature discrimination," Geocarto Int., vol. 16, pp. 27–38, 2001.
P. Maillard, "Comparing texture analysis methods through classification," Photogrammetric Eng. & Rem. Sens., vol. 69, pp. 357–367, 2003.
S. E. Franklin, R. J. Hall, L. M. Moskal, A. J. Maudie and M. B. Lavigne, "Incorporating texture into classification of forest species composition from airborne multispectral images," Int. J. Rem. Sens., vol. 21, pp. 61–79, 2000.
Q. Chen and G. Peng, "Automatic variogram parameter extraction for textural classification of the panchromatic IKONOS imagery," IEEE Trans. Geo. and Rem. Sens., vol. 42, pp. 1106–1115, 2004.
T. R. Reed and J. M. H. Buf, "Review of recent texture segmentation and feature extraction techniques," Comp. Vision, Image Proc. Graphics, vol. 57, pp. 359–372, 1993.
M. R. Turner, "Texture transformation by Gabor function," Bio. Cyber., vol. 55, pp. 71–82, 1986.
W. Y. Ma and B. S. Manjunath, "A texture thesaurus for browsing large aerial photographs," J. Amer. Soc. Info. Sci., vol. 49, pp. 633–648, 1998.
J. Li and R. M. Narayanan, "Integrated spectral and spatial information mining in remote sensing imagery," IEEE Trans. Geo. Rem. Sens., vol. 42, 673–685, 2004.
M. Pesaresi and J.A. Benediktsson, "A new approach for the morphological segmentation of high-resolution satellite imagery," IEEE Trans. Geo. Rem. Sens., vol. 39, pp. 309–320, 2001.
A. Weisberg, M. Najarian, B. Borowski, J. Lisowski and B. Miller, "Spectral angle automatic cluster routine (SAALT): an unsupervised multispectral clustering algorithm," in Proc. IEEE Aerospace 1999 (Aspen, CO, 1999), pp. 307–317.
B. Gorte and A. Stein, "Bayesian classification and class area estimation of satellite images using stratification," IEEE Trans. on Geo. Rem. Sens., vol. 36, pp. 803–812, 1998.
J. R. Quinlan, "Induction of decision trees," Machine learning, vol. 1, pp. 81–106, 1986.
A. Pitiot, A. W. Toga, N. Ayache and P. Thompson. "Texture based MRI segmentation with a two-stage hybrid neural classifier," in Proc. 2002 Int. Joint Conf. Neural Networks (Honolulu, HI, 2002), vol. 3, pp. 2053–2058.
S. Wold, E. Kim and G. Paul, "Principal component analysis," Chemometrics Intell. Lab. Sys., vol. 2, 37–52, 1987.
R. Herbrich, T. Graepel and C. Campbell, "Bayes point machines," J. Mach. Learning Res., vol. 1, pp. 245–279, 2001.
D. Choudhary, A. K. Singh, S. Tiwari and V. P. Shukla, "Performance analysis of texture image classification using wavelet feature," Int. J. Image, Graphics & Sig. Processing (IJIGSP), vol. 5, no. 1, pp.58–63, 2013. DOI: 10.5815/ijigsp.2013.01.08.
U. Babu, V. V. Kumar and B Sujatha, "Texture classification based on texton features," Int. J. Image, Graphics & Sig. Processing (IJIGSP), vol. 4, no. 8, pp.36–42, 2012. DOI: 10.5815/ijigsp.2012.08.05.
J. A. Hartigan and M. A. Wong. "Algorithm AS 136: A k-means clustering algorithm," Appl. Stat., pp. 100–108, 1979.
T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman and A. Y. Wu, "An efficient k-means clustering algorithm: analysis and implementation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, pp. 881–892, 2002.
S. Ray and R. H. Turi, "Determination of number of clusters in k-means clustering and application in color image segmentation," in Proc. Int. Conf. Advances in Pattern Recogn. Digital Tech. 1999 (Calcutta, India, 1999), pp. 137–143.
P. S. Bradley and U. M. Fayyad, "Refining initial points for k-means clustering," in Proc. Int. Conf. Mach. Learning, J. Shavlik, Ed., San Francisco, CA, 1998, pp. 91–99.
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines. Cambridge, UK: The Cambridge University Press, 2000.
E. Chang, K. Goh, G. Sychay and G. Wu, "CBSA: Content-based soft annotation for multimodal image retrieval using Bayes point machines," IEEE Trans. Cir. and Sys. Video Tech., vol. 13, pp. 26–38, 2003.
P. Mantero, G. Moser and S. B. Serpico, "Partially supervised classification of remote sensing images through SVM-based probability density estimation," IEEE Trans. Geo. Rem. Sens., vol. 43, pp. 559–570, 2005.
T. Celik and T. Tjahjadi, "Bayesian texture classification and retrieval based on multiscale feature vector," Pattern Recogn. Let., vol. 32, pp.159–167, 2011.
C. Rodarmel and J. Shan, "Principal component analysis for hyperspectral image classification," Surveying Land Info. Sci., vol. 62, pp. 115–122, 2002.
B. S. Manjunath and W. Y. Ma, "Texture features for browsing and retrieval of image data," IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, pp. 837–842, 1996.
B. S. Manjunath, P. Wu, S. Newsam and H. D. Shin, "A texture descriptor for browsing and similarity retrieval," J. Sig. Proc.: Image Comm., vol. 16, pp. 33–43, 2000.
R. Herbrich, T. Graepel and C. Campbell, "Bayes point machines: Estimating the Bayes point in kernel space," In Proc. IJCAI 1999 Workshop on Support Vector Machines (Stockholm, Sweden, 1999), pp. 23–27.
T. Minka, J. Winn, J. Guiver and D. Knowles, Infer.NET 2.6, Microsoft Research Cambridge, 2014. http://research.microsoft.com/infernet.
J. Li, "Texture classification of Landsat TM imagery using Bayes Point Machine," In Proc. ACMSE 2013 (Savannah, GA, 2013), pp. 16.1–16.6.
C-C. Chang and C-J. Lin, "LIBSVM: a library for support vector machines," ACM Trans. Intel. Sys. Tech., vol. 2, pp. 27.1–27.27, 2011.