Estimating Target Signatures with Diverse Density
This addresses the difficulty of obtaining reliable target signatures for hyperspectral detection, but it is incremental as it builds on existing diverse density approaches.
The paper tackles the problem of learning effective target signatures for hyperspectral target detection from training data with uncertain and imprecise groundtruth, using a diverse density-based method, and shows results on simulated and real data.
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.