Quantitative comparison of quadratic covariance-based anomalous change detectors.
Simulations applied to hyperspectral imagery from the AVIRIS sensor are employed to quantitatively evaluate the performance of anomalous change detection algorithms. The evaluation methodology reflects the aim of these algorithms, which is to distinguish actual anomalous changes in a pair of images from the incidental differences that pervade the entire scene. By simulating both the anomalous changes and the pervasive differences, accurate and plentiful ground truth is made available, and statistical estimates of detection and false alarm rates can be made. Comparing the receiver operating characteristic (ROC) curves that encapsulate these rates provides a way to identify which algorithms work best under which conditions.
Theiler
James
J
Space and Remote Sensing Sciences, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA. jt@lanl.gov