Local decision maximum margin metric learning for hyperspectral target detection

Detecting certain targets from hyperspectral images (HSIs) is of great interest for both civilian and military applications, with the aim being to detect and identify target pixels based on specific spectral signatures. However, the classical algorithms are generally dependent on the specific statistical hypothesis test, and the algorithms may only perform well with certain assumptions. Therefore, in this paper, a novel metric-learning-based target detection framework, named local decision maximum margin metric learning (LDM³L), is proposed for HSI target detection. The proposed method can better separate the target samples from background ones, without the need for certain assumptions. The experimental results demonstrate that the proposed method outperforms both the state-of-the-art target detection algorithms and the other classical metric learning methods.