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Signature-based detectors for hyperspectral target detection rely on knowingthe specific target signature in advance. However, target signature are oftendifficult or impossible to obtain. Furthermore, common methods for obtainingtarget signatures, such as from laboratory measurements or manual selectionfrom an image scene, usually do not capture the discriminative features oftarget class. In this paper, an approach for estimating a discriminative targetsignature from imprecise labels is presented. The proposed approach maximizesthe response of the hybrid sub-pixel detector within a multiple instancelearning framework and estimates a set of discriminative target signatures.After learning target signatures, any signature based detector can then beapplied on test data. Both simulated and real hyperspectral target detectionexperiments are shown to illustrate the effectiveness of the method.

Multiple Instance Hybrid Estimator for Learning Target Signatures