International Transaction of Electrical and Computer Engineers System
ISSN (Print): 2373-1273 ISSN (Online): 2373-1281 Website: Editor-in-chief: Dr. Pushpendra Singh, Dr. Rajkumar Rajasekaran
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International Transaction of Electrical and Computer Engineers System. 2017, 4(1), 1-7
DOI: 10.12691/iteces-4-1-1
Open AccessArticle

A Graph-based Technique for the Spectral-spatial Hyperspectral Images Classification

F. Poorahangaryan1, H. Beheshti1 and S.A. Edalatpanah1,

1Department of Electrical and Computer Engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran

Pub. Date: February 08, 2017

Cite this paper:
F. Poorahangaryan, H. Beheshti and S.A. Edalatpanah. A Graph-based Technique for the Spectral-spatial Hyperspectral Images Classification. International Transaction of Electrical and Computer Engineers System. 2017; 4(1):1-7. doi: 10.12691/iteces-4-1-1


Minimum Spanning Forest (MSF) is a graph-based technique used for segmenting and classification of images. In this article, a new method based on MSF is introduced that can be used to supervised classification of hyperspectral images. For a given hyperspectral image, a pixel-based classification, such as Support Vector Machine (SVM) or Maximum Likelihood (ML) is performed. On the other hand, dimensionality reduction is carried out by Principal Components Analysis (PCA) and the first eight components are considered as the reference data. The most reliable pixels, which are obtained from the result of pixel-based classifiers, are used as markers in the construction of MSF. In the next stage, three MSF’s are created after considering three distinct criteria of similarity (dissimilarity). Ultimately, using the majority voting rule, the obtained classification maps are combined and the final classification map is formed. The simulation results presented on an AVRIS image of the vegetation area indicate that the proposed technique enhanced classification accuracy and provides an accurate classification map.

hyperspectral images spectral-spatial classification minimum spanning forest (MSF) segmentation

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[1]  L. J. P. Maaten, E. O. Postma, and H. J. Herik, “Dimensionality reduction:A comparative review,” Univ. Maastricht, Amsterdam, The Netherlands, Tech. Rep, 2009.
[2]  S. Moussaoui, H. Hauksdottir, F. Schmidt, C. Jutten, J. Chanussot,D. Brie, S. Douté, and J. A. Benediktsson, “On the decomposition of Mars hyperspectral data by ICA and Bayesian positive sourceseparation,” Neurocomputing, vol. 71, no. 10-12, pp. 2194-2208, 2008.
[3]  R. Dianat and Sh. Kasaei, “Dimension Reduction of Optical RemoteSensing Images via Minimum Change Rate Deviation Method,” IEEE Trans.Geosci. Remote Sens., Vol. 48, no. 1, pp. 198-206, 2010.
[4]  H. Huang, J. Liu and Y. Pan, “Semi-Supervised Marginal Fisher Analysisfor Hyperspectral Image Classification,” XXII ISPRS Congress, pp.377-382, 2012.
[5]  D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. NewYork: Wiley, 2003.
[6]  H. Zhou, Z. Mao, and D. Wang, “Classification of coastal areas by airborne hyperspectral image,” in Proc. SPIE Opt. Technol. Atmos., Ocean,Environ. Stud., vol. 5832, pp. 471-476, 2005.
[7]  C.H. Li, B.C. Kuo, C.T.Lin and C. S. Huang, “A Spatial–Contextual Support Vector Machinefor Remotely Sensed Image Classification,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 3, pp. 784-799, 2012.
[8]  M. Fauvel, J. Chanussot, and J. A. Benediktsson, “Evaluation of kernels for multiclass classification of hyperspectral remote sensing data,” in Proc. ICASSP, pp. II-813-II-816, 2006.
[9]  Mountrakis, G., Im, J., & Ogole, C., “Support vector machines in remote sensing: A review.” ISPRS Journal of Photogrammetry and Remote Sensing, vol.66, no. 3, pp. 247-259, 2011.
[10]  F. Mirzapour and H. Ghassemian, “Improving hyperspectral image classification by combining spectral, texture, and shape featuresImproving hyperspectral image classification by combining spectral, texture, and shape features”, International Journal of Remote Sensing, vol 36, no. 4, pp. 1070-1096, Feb. 2015.
[11]  M. Dalla Mura, J.A. Benediktsson B. Waske and L. Bruzzone, “Extended profiles with morphological attribute filters for the analysis classification of hyperspectral data ,” Int. J. Remote Sens., vol. 31, no. 22, pp. 5975-5991,2010.
[12]  J.. Li, J. M. Bioucas-Dias, and A. Plaza, “Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields,” IEEE Trans. Geos. Remote Sens., vol. 50, no. 3, pp. 809-823, 2012.
[13]  M. Borhani and H. Ghassemian, “Hyperspectral Image Classification Based on Spectral-Spatial Features Using Probabilistic SVM and Locally Weighted Markov Random Fields ”, Iranian Conference on Intelligent Systems (ICIS 2014), pp. 1-6, 2014.
[14]  Y. Tarabalka, J, Chanussot, and A. Benediktsson, “ Segmentation and classification of hyperspectral images using watershed transformation,” J. Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, 2010.
[15]  Y. Tarabalka, J. A. Benediktsson, and J, Chanussot, “Spcetral-spatial classification of hyperspectral imagery based on partitional clustering techniques,” IEEE Trans. Geo. and Remote Seng, vol. 47, no. 8, pp, 2373-2987, 2009.
[16]  Y. Tarabalka, J. Chanussot, and J.A. Benediktsson, “Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers,” IEEE Trans. Systems, Man, and Cybernetics: Part B, vol. 40, no. 5, pp. 1267-1279, Oct. 2010.
[17]  Y. Tarabalka, J.A. Benediktsson, J. Chanussot, and J.C. Tilton, “Multiple spectral-spatial classification approach for hyperspectral data,” IEEE Trans. Geosci. Remote Sens, vol. 48, no. 11, pp. 4122-4132, Nov. 2010.
[18]  K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot and J. A. Benediktsson, “Spectral–Spatial Classification of Hyperspectral Data Based on a Stochastic Minimum Spanning Forest Approach,” IEEE Trans. Geosci. Remote Sens, vol. 21, NO. 4, pp. 2008-2021, APRIL 2012.
[19]  R. Pike, S. K. Patton, G. Lu, L. V. Halig, D. Wang, Z. G. Chen, and B. Fei, “A Minimum Spanning Forest Based Hyperspectral Image Classification Method for Cancerous Tissue Detection”, Conference: Proc. SPIE 9034, Medical Imaging 2014.
[20]  Cortes, C.; Vapnik, V. “Support-vector networks”. Machine Learning, vol 20, no. 3. pp 273-297, 1995.
[21]  R.O. Duda, P.e. Hart, and D.G. Stock, “Pattern classification”, Second Edition, wiely, 2001.
[22]  T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 2nd ed. Cambridge, MA: MIT Press, 2001.