Stability of robust and non-robust principal components analysis
Computational Statistics & Data Analysis
Robust locally linear embedding
Pattern Recognition
M-estimators of roughness and scale for GA0-modelled SAR imagery
EURASIP Journal on Applied Signal Processing
Statistical functions and procedures in IDL 5.6 and 6.0
Computational Statistics & Data Analysis
Image denoising: a nonlinear robust statistical approach
IEEE Transactions on Signal Processing
Generalized Mean-Median Filtering for Robust Frequency-Selective Applications
IEEE Transactions on Signal Processing
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Remote sensing data present peculiar features and characteristics that may make their statistical processing and analysis a difficult task. Among them, it can be mentioned the volume of data involved, the redundancy, the presence of unexpected values that arise mainly due to noisy pixels and background objects whose responses to the sensor are very different from those of their neighbours. Sometimes, the volume of data and number of variables involved is so large that any statistical analysis becomes unmanageable if data are not condensed in some way. A commonly used method to deal with this situation is Principal Component Analysis (PCA) based on classical statistics: sample mean and covariance matrices. The drawback in using sample covariance or correlation matrices as measures of variability is their high sensitivity to spurious values. In this work we analyse and evaluate the use of some Robust Principal Component techniques and make a comparison of Robust and Classical PCs performances when applied to satellite data provided by the hyperspectral sensor AVIRIS (Airborne Visible/Infrared Imaging Spectrometer). We conclude that some robust approaches are the most reliable and precise when applied as a data reduction technique before performing supervised image classification.