{"title":"Approach Based on Fuzzy C-Means for Band Selection in Hyperspectral Images","authors":"Diego Saqui, Jos\u00e9 H. Saito, Jos\u00e9 R. Campos, L\u00facio A. de C. Jorge","volume":113,"journal":"International Journal of Computer and Information Engineering","pagesStart":889,"pagesEnd":896,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10004410","abstract":"
Hyperspectral images and remote sensing are important for many applications. A problem in the use of these images is the high volume of data to be processed, stored and transferred. Dimensionality reduction techniques can be used to reduce the volume of data. In this paper, an approach to band selection based on clustering algorithms is presented. This approach allows to reduce the volume of data. The proposed structure is based on Fuzzy C-Means (or K-Means) and NWHFC algorithms. New attributes in relation to other studies in the literature, such as kurtosis and low correlation, are also considered. A comparison of the results of the approach using the Fuzzy C-Means and K-Means with different attributes is performed. The use of both algorithms show similar good results but, particularly when used attributes variance and kurtosis in the clustering process, however applicable in hyperspectral images.<\/p>\r\n","references":"[1]\tE. Novo, Sensoriamento Remoto: Princ\u00edpios e Aplica\u00e7\u00f5es, 4 ed., Edgard Bl\u00fccher, 2011.\r\n[2]\tC. I. Chang, Techniques for Spectral Detection and Classification, New York: Kluwer Academic\/Plenum Publishers, 2003.\r\n[3]\tL. A. C. Jorge and R. Y. Inamasu, \"Uso de ve\u00edculos a\u00e9reos n\u00e3o tripulados (VANT) em Agricultura de Precis\u00e3o,\" in Agricultura de Precis\u00e3o. Resultados de um Novo Olhar., Embrapa, 2014.\r\n[4]\tF. Baret, G. Guyot and D. J. Major, \"TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation,\" International Geoscience and Remote Sensing Symposium (IGARSS'89); Canadian Symposium on Remote Sensing. IEEE, 1989., pp. 1355-1358, 1989.\r\n[5]\tC. I. Chang and S. Wang, \"Constrained band selection for hyperspectral imagery,\" IEEE Transactions on Geosciences and Remote Sensing, vol. 44, no. 6, pp. 1575-1585, 2006.\r\n[6]\tG. Petrie, P. Heasler, and T. Warner, \u201cOptimal band selection strategies for hyperspectral data sets,\u201d Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International, vol. 3, pp. 1582-1584, July, 1998.\r\n[7]\tP. Groves and P. Bajcsy, \u201cMethodology for hyperspectral band and classification model selection,\u201d IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, pp. 120-128, 2003.\r\n[8]\tH. Du, H. Qi, X. Wang, R. Ramanath, and W. E. Snyder, \u201cBand selection using independent component analysis for hyperspectral image processing,\u201d in Applied Imagery Pattern Recognition Workshop, IEEE Computer Society, Los Alamitos, CA, USA, pp. 93- 98, 2003.\r\n[9]\tA. M. U. Uso, F. Pla, J. M. Sotoca, and P. G. Sevilla, \u201cClustering based hyperspectral band selection using information measures,\u201d IEEE Transactions on Geosciences and Remote Sensing, vol. 45, pp.158-4171, Dec. 2007.\r\n[10]\tM. Sohaib, I. U. Haq and Q. Mushtaq, \"Dimensional Reduction of Hyperspectral Image Data Using Band Clustering and Selection Based on Statistical Characteristics of Band Images,\" International Journal of Computer and Communication Engineering, vol. 2, Mar\u00e7o 2013.\r\n[11]\tA. M. U. Uso, F. Pla, J. M. Sotoca and P. G. Sevilla, \"Clustering based hyperspectral band selection using information measures,\" IEEE Transactions on Geosciences and Remote Sensing, vol 45, 2007.\r\n[12]\tD. Landgrebe, \"Multispectral Data Analysis: A Signal Theory Perspective,\" School of Electr. Comput. Eng., Purdue Univ., West Lafayette, 1998.\r\n[13]\tT. Boggs, Specytral Python (SPy). URL:http:\/\/www.spectralpython.net, 2014. Accessed April 2016.\r\n[14]\tC. I. Chang, Hyperspectral data Processing: Algorithm Design and Analysis, Maryland, USA: Wiley, 2013.\r\n[15]\tA. K. Jain and R. C. Dubes, Algorithms for Clustering Data, Prentice Hall, 1988.\r\n[16]\tJ. C. Dunn, \"A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters,\" Journal of Cybernetics, vol. 3, pp. 32-57, 1973.\r\n[17]\tJ. C. Bezdek, \"Pattern Recognition with Fuzzy Objective Function Algorithms,\" Plenum Press, 1981.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 113, 2016"}