Metodología para la determinación de usos del suelo mediante procesamiento de imágenes satelitales
Keywords:
Remote sensing, Satelital image, Classification, Principal components analysis, Wavelet, Eigenvalue, Eigenvector
Abstract
Satellital systems orbiting around earth allows continuous monitoring of some phenomenon ocurring on its surface at differerent latitudes. Specialized image processing techniques make possible to obtain specific information about the nature of soils. This paper presents software tools and a series of recommendations for automatic classification of soils, based on the wavelet transform and principal components analysis techniques.
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References
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[28] Heij Christiaan et al, Forecast comparision of principal component regression and principal covariate regression, Computational Statistics & Data analysis 51 (2007) 3612-3625
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[30] Tamura Masayuki, Tsujita Shinsuke, A study on the number of principal components and sensitivity of fault detection using PCA, Computers and chemical Engineering 31 (2007) 1035-1046
[31] Salinelli Ernesto, Nonlinear principal components , II: Characterization of normal distributions, Journal of Multivariate Analysis 100(2009) 652-660
[32] Chester Victoria, Wrigley Allan, The identification of age-related differences in kinetic gait parameters using principal component analysis, Clinical Biomechanics 23 (2008) 212-220
[33] Keithley Richard et al, Multivariate concentration determination using principal component regression with residual analysis, Trends in analytical chemistry Vol. 28, No 9, 2009
[34] Boik Robert, Shirvani Amin, Principal components on coefficient of variation matrices, Statistical Methodology 6 (2009) 21-46
[2] Seminario diez años después de ley 388 de 1997 ponente Gina Parody: Bogotá disponible en Internet en http://www.ginaparody.com/eventos/seminario_ ley_188.htm
[3] E, Vargas . Análisis y clasificación del uso y cobertura de la tierra con interpretación de Imágenes: Bogota: Instituto Geográfico Agustín Codazzi, 1992. 120P
[4] R Lombo: Ecología y Usos del Suelo [revisado mayo 10 2008] disponible en internet URL: http://www.sogeocol.edu.co/documentos/Ecologia.pdf
[5] J Jensen R. Introductory Digital Image Processing Second edition: USA: Prentice Hall, 1996. 318P
[6] F. Kuehn. Remote sensing for site characterization, USA: Editorial Springer, 2000
[7] R.Vincent Fundamentals of geological and environmental remote sensing USA: Prentice Hall, 1997. 285P
[8] H. Araki. Fusao de informacoes espectrais, altimetricas e de dados auxiliares na classificacao de imagens de alta resolucao espacial, Universidade Federal do Paraná, Curitiba 2005. Tesis doctoral Facultad de Geodesia y ciencias de la información
[9] J Donnay. Remote Sensing and Urban Analysis, Premier edition. London: Taylor y Francis, 2001. 268P
[10] P. Gibson, C. Power., Introductory Remote Sensing Principles and Concepts V.2: Routledge. London, 2000. 250P
[11] J. Richards Remote Sensing Digital Image Analysis: An introduction, Berlin: Ed Springer Verlag, 1993. 320P
[12] J Walker. A primer on wavelets and their Scientific applications USA: Editorial CRC, 1999 380P
[13] V. Vega. Aplicación de la Transformada Wavelet discreta (TWD) en la detección e identificación de eventos de la calidad de la energía eléctrica, Bucaramanga. UIS, 2004 Tesis de Pregrado Facultad de Ingeniería Eléctrica, Electrónica y Telecomunicaciones
[14] R Gonzalez, R. Woods. Digital Image Processing using Matlab, Upper Saddle River : Pearson – Prentice Hall, 2004. 954P
[15] I. Daubechies. Teen Lectures on Wavelets, USA, SIAM 1992
[16] S. Mallat. A wavelet tour of signal processing. Second edition, USA Prentice Hall, 1999. 250P
[17] P. Addison. The illustrated Wavelet transform Handbook. Second Edition. London Institute of Physics, 2002. 420P
[18] F Tadeo, T., Álvarez, S. González. Seguimiento de la cristalización de azúcar mediante procesado de imagen con wavelets Valladolid, Universidad de Valladolid 2002 Tesis de pregrado Departamento de Sistemas y Automática
[19] Y Tang, I Yang. Wavelet theory and its application to pattern recognition, USA: World Scientific, 2000. 456P
[20] R Medina, I. Lizarazo, Fusión de imágenes satelitales usando la transformadora de wavelet, Bogota: Universidad Francisco José de Caldas, 2004. 80P
[21] Wavelet smoothing of fMRI activation images” [online], 2002 [citado: Agosto de 2008]. University College ,London. Disponible desde:www.fil.ion.ucl. ac.uk/~wpenny/publications/wave.pdf
[22] B Bruegge, Object-oriented Software Engineering: Using UML, Patterns and Java, Second Edition, Carnegie Mellon University.2005
[23] E Braude, Ingenieria de Software- Una perspectiva orientada a objetos.Alfaomega.2000
[24] Lu We-Zhen et al, Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis,Building and Environment (2010)
[25] Boente Graciela et al, Detecting influential observations in principal components and common principal components, Computational Statistics and data analysis 54 (2010) 2976-2975
[26] Ravi V, Pramodh C, Threshold accepting trained principal component neural network and feautre subset selection: Application to bankruptcy prediction in Banks, Applied soft computing 8 (2008) 1539-1548
[27] Cho chien-wen et al, A visión-based analysis system for gait recognition in patients with Parkinson’s disease, Expert systems with applications 36 (2009) 7033-7039
[28] Heij Christiaan et al, Forecast comparision of principal component regression and principal covariate regression, Computational Statistics & Data analysis 51 (2007) 3612-3625
[29] Al-Alawi Saleh et al, Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone.
[30] Tamura Masayuki, Tsujita Shinsuke, A study on the number of principal components and sensitivity of fault detection using PCA, Computers and chemical Engineering 31 (2007) 1035-1046
[31] Salinelli Ernesto, Nonlinear principal components , II: Characterization of normal distributions, Journal of Multivariate Analysis 100(2009) 652-660
[32] Chester Victoria, Wrigley Allan, The identification of age-related differences in kinetic gait parameters using principal component analysis, Clinical Biomechanics 23 (2008) 212-220
[33] Keithley Richard et al, Multivariate concentration determination using principal component regression with residual analysis, Trends in analytical chemistry Vol. 28, No 9, 2009
[34] Boik Robert, Shirvani Amin, Principal components on coefficient of variation matrices, Statistical Methodology 6 (2009) 21-46
Published
2013-11-19
How to Cite
Sanabria Sarmiento, J., & Zabala Vargas, S. (2013). Metodología para la determinación de usos del suelo mediante procesamiento de imágenes satelitales. ITECKNE, 7(1), 98-107. https://doi.org/https://doi.org/10.15332/iteckne.v7i1.2714
Issue
Section
Research and Innovation Articles
