【摘要】 在现有基于物理模型的高光谱异常探测HAD(Hyperspectral Anomaly Detection)方法中，低秩表示LRR(Low-Rank Representation)模型以其强大的背景和目标特征分离能力而受到广泛的关注和采用。然而，由于依赖手动参数的选择以及较差的泛化性，导致其实际应用受到限制。为此，本文将LRR模型与深度学习技术相结合，提出了一种新的适用于HAD的基础深度展开网络，称为LRR-Net。该方法借助交替方向乘法ADMM(Alternating Direction Method of Multipliers)优化器高效地求解LRR模型，并将其求解步骤耦合至深度网络中以指导其搜索过程，为深度网络提供了一定的理论基础，具有较强的可解释性。此外，LRR-Net以端到端的方式将一系列正则化的参数转换为可学习的网络参数，从而避免了手动调参。4组不同的高光谱异常探测实验证明了LRR-Net的有效性，与其他无监督的异常探测方法相比，LRR-Net具有较强的泛化性和鲁棒性，能够提高HAD的精度。更多还原

【Abstract】 Hyperspectral Anomaly Detection(HAD) is one of the most critical topic in hyperspectral remote sensing and has been extensively addressed in the literature over the past decade. Among them, Low-Rank Representation(LRR) models are widely used owing to their powerful separation ability for the background and targets. But their applications in practical situations still remain limited due to the extreme dependence on manual parameter selection and relatively poor generalization ability. To this end, this paper combines the LRR model with deep learning techniques to propose a new underlying network for HAD, called LRR-Net. This method efficiently solves the LRR model with the help of the Alternating Direction Method of Multipliers(ADMM) optimizer, and incorporates the solution as a priori knowledge into the deep network to guide the optimization of parameters, providing a theoretical basis for deep networks. In addition, LRRNet converts a series of regularized parameters into learnable network parameters in an end-to-end manner, thus avoiding manual tuning of parameters. Experimental results obtained from publicly available datasets and our datasets demonstrate that the LRR-Net method outperforms many state-of-the-art model-based and deep-based algorithms of hyperspectral anomaly detection. Overall, deep learning networks are powerful in learning and are robust compared to traditional models in processing datasets with different complexity. However,despite the strong fitting ability of deep learning data, the necessary prior information is lacking, which often makes the algorithm fall into the local optima, which leads to the failure of deep learning to guarantee the stability of HAD results. The model-based algorithm can better make up for this defect, which can often get better results by improving the separability between the background and the target. Nonetheless,these LRR-based methods are unable to effectively suppress background noise due to their limited representation power, such as shadows,trees, and edges in complex scenes, with relatively large volatility in detection effects. The LRR-Net presented in this paper combines the advantages of the above two methods, and the experimental results of four typical scenarios show that the search of the optimal parameters in the neural network can effectively solve the HAD problem in an adaptive way, which is more physically meaningful.更多还原