Maximum margin metric learning based target detection for hyperspectral images

Target detection is one of the most important problems in hyperspectral image (HSI) processing. However, the classical algorithms depend on the specific statistical hypothesis test, and the algorithms may only perform well under certain conditions, e.g., the adaptive matched subspace detector algorithm assumes that the background covariance matrices do not include the target signatures, which seldom happens in the real world. How to develop a proper metric for measuring the separability between targets and backgrounds becomes the key in target detection. This paper proposes an efficient maximum margin metric learning (MMML) based target detection algorithm, which aims at exploring the limited samples in metric learning and transfers the metric learning problem for hyperspectral target detection into a maximum margin problem which can be optimized via a cutting plane method, and maximally separates the target samples from the background ones. The extensive experimental results with different HSIs demonstrate that the proposed method outperforms both the state-of-the-art target detection algorithms and the other classical metric learning methods.