Title :
Principal components analysis in remote sensing
Author_Institution :
EROS Data Center, UNEP/GRID, Sioux Falls, SD, USA
Abstract :
In remote sensing applications principal components analysis (PCA) is usually performed by using the covariance matrix. However, the analysis of results, using different remote sensing sensor systems, showed a significant improvement in the signal to noise ratio (SNR) by using the correlation matrix in comparison to the covariance matrix. The paper reviews the studies and discusses the application of PCA for the analysis of anomalies and trends in long time series images
Keywords :
correlation methods; geophysical techniques; geophysics computing; image processing; remote sensing; statistical analysis; correlation matrix; long time series images; principal components analysis; remote sensing; signal to noise ratio; Covariance matrix; Earth; Image analysis; Principal component analysis; Remote sensing; Satellite broadcasting; Sensor systems; Signal analysis; Signal to noise ratio; Time series analysis;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 1993. IGARSS '93. Better Understanding of Earth Environment., International
Conference_Location :
Tokyo
Print_ISBN :
0-7803-1240-6
DOI :
10.1109/IGARSS.1993.322441
