VERIFICATION OF STATISTICAL PROPERTIES FOR HYPERSPECTRAL IMAGES: HETEROSCEDASTICITY AND NON-STATIONARITY

This paper investigates the heteroscedasticity and nonstationarity, two statistical properties, of hyperspectral remote sensing data. In the field of mathematical sciences, a collection of variables is heteroscedastic if there are sub-populations that have different variances or volatilities than others, while a non-stationary process refers to a stochastic process whose joint probability distribution are changing when shifted in time or space. To be treat as sequences, hyperspectral data are investigated via Bartlett Test and WaldWolfowitz Runs Test to verify the heteroscedasticity and non-stationarity, respectively. Most experimental results fail to pass Bartlett Test and Wald-Wolfowitz Runs Rest statistically significant, indicating that both heteroscedasticity and non-stationarity are intrinsic properties of spectral response sequence.