Abstract
Since hyperspectral imagery (HSI) (or remotely sensed data) provides more information (or additional bands) than traditional gray level and color images, it can be used to improve the performance of image classification applications. A hyperspectral image presents spectral features (also called spectral signature) of regions in the image as well as spatial features. Feature reduction, selection, and transformation has been a challenging problem for hyperspectral image classification due to the high number of dimensions. In this paper, we firstly use Random Forest (RF) algorithm to select significant features and then apply Kernel Fukunaga Koontz Transform (K-FKT), a non-linear statistical technique, for the classification. We provide our experimental results on AVIRIS hyperspectral image dataset that contains various types of field crops. In our experimental results, we have obtained overall classification accuracy around 84 percent for the classification of 16 types of field crops.
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References
Binol, H., Bal, A., Dinc, S.: Classification on hyperspectral images using enhanced covariance descriptor. In: 20th Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2012)
Benediktsson, J.A., Palmason, J.A., Member, S., Sveinsson, J.R., Member, S.: Sveinsson, classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing 43, 480–491 (2005)
Banerjee, A., Burlina, P., Diehl, C.: A support vector method for anomaly detection in hyperspectral imagery. IEEE T. Geoscience and Remote Sensing, 2282–2291 (2006)
Borges, J.S., Bioucas-Dias, J.M., Marçal, A.R.S.: Bayesian hyperspectral image segmentation with discriminative class learning. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4477, pp. 22–29. Springer, Heidelberg (2007)
Alam, M.S., Islam, M.N., Bal, A., Karim, M.A.: Hyperspectral target detection using gaussian filter and post-processing. Optics and Lasers in Engineering 46(11), 817–822 (2008)
Du, P., Zhang, W., Xia, J.: Hyperspectral remote sensing image classification based on decision level fusion. Chin. Opt. Lett. 9(3), 031002 (2011)
Tuia, D., Camps-Valls, G.: Urban Image Classification With Semisupervised Multiscale Cluster Kernels. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1 (2011)
Samiappan, S., Prasad, S., Bruce, L.M.: Automated hyperspectral imagery analysis via support vector machines based multi-classifier system with non-uniform random feature selection. In: IGARSS, pp. 3915–3918. IEEE (2011)
Aviris hyperspectral data (2012), http://aviris.jpl.nasa.gov/html/data.html
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Verikas, A., Gelzinis, A., Bacauskiene, M.: Mining data with random forests: A survey and results of new tests. Pattern Recognition 44(2), 330–349 (2011)
Cumbaa, C., Jurisica, I.: Protein crystallization analysis on the world community grid. Journal of Structural and Functional Genomics 11, 61–69 (2010), doi:10.1007/s10969-009-9076-9
Fukunaga, K., Koontz, W.L.G.: Application of the karhunen-loéve expansion to feature selection and ordering. IEEE Trans. Comput. 19(4), 311–318 (1970)
Ochilov, S., Alam, M.S., Bal, A.: Fukunaga-koontz transform based dimensionality reduction for hyperspectral imagery. Proceedings-SPIE The International Society For Optical Engineering 6233, 62332A–62332A–8 (2006)
Li, Y.H., Savvides, M.: Kernel fukunaga-koontz transform subspaces for enhanced face recognition. 2012 IEEE Conference on Computer Vision and Pattern Recognition 0, 1–8 (2007)
Dinc, S., Bal, A.: A statistical approach for multiclass target detection. Procedia Computer Science, Complex A1daptive Sysytems 6(0), 225–230 (2011)
Hyperspectral remote sensing scenes, aviris sensor (indian pines) (2013), http://www.ehu.es/ccwintco/index.php/Sensores-hiperespectrales
Bagan, H., Yasuoka, Y., Endo, T., Wang, X., Feng, Z.: Classification of airborne hyperspectral data based on the average learning subspace method. IEEE Geoscience and Remote Sensing Letters 5(3), 368–372 (2008)
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Dinç, S., Aygün, R.S. (2013). Evaluation of Hyperspectral Image Classification Using Random Forest and Fukunaga-Koontz Transform. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_18
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DOI: https://doi.org/10.1007/978-3-642-39712-7_18
Publisher Name: Springer, Berlin, Heidelberg
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