  In this work we propose a method to find anomalous changes in remote sensing
images based on the chronochrome approach. A regressor between images is used
to discover the most {\em influential points} in the observed data. Typically,
the pixels with largest residuals are decided to be anomalous changes. In order
to find the anomalous pixels we consider the Cook distance and propose its
nonlinear extension using random Fourier features as an efficient nonlinear
measure of impact. Good empirical performance is shown over different
multispectral images both visually and quantitatively evaluated with ROC
curves.
