  Hyperspectral target detection algorithms rely on knowing the desired target
signature in advance. However, obtaining an effective target signature can be
difficult; signatures obtained from laboratory measurements or
hand-spectrometers in the field may not transfer to airborne imagery
effectively. One approach to dealing with this difficulty is to learn an
effective target signature from training data. An approach for learning target
signatures from training data is presented. The proposed approach addresses
uncertainty and imprecision in groundtruth in the training data using a
multiple instance learning, diverse density (DD) based objective function.
After learning the target signature given data with uncertain and imprecise
groundtruth, target detection can be applied on test data. Results are shown on
simulated and real data.
