Principal component analysis applied to remote sensing

[EN] The main objective of this article was to show an application of principal component analysis (PCA) which is used in two science degrees. Particularly, PCA analysis was used to obtain information of the land cover from satellite images. Three Landsat images were selected from two areas which were located in the municipalities of Gandia and Vallat, both in the Valencia province (Spain). In the first study area, just one Landsat image of the 2005 year was used. In the second study area, two Landsat images were used taken in the 1994 and 2000 years to analyse the most significant changes in the land cover. According to the results, the second principal component of the Gandia area image allowed detecting the presence of vegetation. The same component in the Vallat area allowed detecting a forestry area affected by a forest fire. Consequently in this study we confirmed the feasibility of using PCA in remote sensing to extract land use information.[ES] El objetivo principal de este artículo es mostrar una aplicación del análisis de componentes principales (PCA) que se utiliza en dos grados de la ciencia. En particular, se utilizó el análisis de PCA para obtener información de la cobertura del suelo a partir de imágenes de satélite. Tres imágenes Landsat fueron seleccionadas a partir de dos áreas que se encuentran en los municipios de Gandia y Vallat, ambos en la provincia de Valencia (España). En la primera área de estudio, se utilizó una sola imagen Landsat del año 2005. En la segunda área de estudio, se utilizaron dos imágenes Landsat tomadas en los años 1994 y 2000 para analizar los cambios más significativos en la cobertura de la tierra. Según los resultados, el segundo componente principal de la imagen de área Gandia permitió la detección de la presencia de vegetación. El mismo componente en el área de Vallat permitió detectar un área forestal afectada por un incendio forestal. En consecuencia, en este estudio se confirmó la viabilidad del uso de PCA en teledetección para extraer la información territorial.Estornell, J.; Martí-Gavliá, JM.; Sebastiá, MT.; Mengual, J. (2013). Principal component analysis applied to remote sensing. Modelling in Science Education and Learning. 6(2):83-89. doi:10.4995/msel.2013.1905SWORD838962Xiuping Jia, & Richards, J. A. (1999). Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification. IEEE Transactions on Geoscience and Remote Sensing, 37(1), 538-542. doi:10.1109/36.739109J. R. Eastman, M. Filk. Long sequence time series evaluation using standardized principal components. Photogrammetric Engineering and Remote Sensing. 59(6) 991-996. (1993)

Principal component analysis applied to remote sensing

The main objective of this article was to show an application of principal component analysis (PCA) which is used in two science degrees. Particularly, PCA analysis was used to obtain information of the land cover from satellite images. Three Landsat images were selected from two areas which were located in the municipalities of Gandia and Vallat, both in the Valencia province (Spain). In the first study area, just one Landsat image of the 2005 year was used. In the second study area, two Landsat images were used taken in the 1994 and 2000 years to analyse the most significant changes in the land cover. According to the results, the second principal component of the Gandia area image allowed detecting the presence of vegetation. The same component in the Vallat area allowed detecting a forestry area affected by a forest fire. Consequently in this study we confirmed the feasibility of using PCA in remote sensing to extract land use information.</p