Resampling approach for anomaly detection in multispectral images

We propose a novel approach for identifying the "most unusual" samples in a data set, based on a resampling of data attributes. The resampling produces a "background class" and then binary classification is used to distinguish the original training set from the background. Those in the training set that are most like the background (i.e., most unlike the rest of the training set) are considered anomalous. Although by their nature, anomalies do not permit a positive definition (if I knew what they were, I wouldn't call them anomalies), one can make "negative definitions" (I can say what does not qualify as an interesting anomaly). By choosing different resampling schemes, one can identify different kinds of anomalies. For multispectral images, anomalous pixels correspond to locations on the ground with unusual spectral signatures or, depending on how feature sets are constructed, unusual spatial textures.