Exploring techniques that breakthrough the unknown space or material species is of considerable significance to military and civilian fields, and it is a challenging task without any prior information. Nowadays, the use of material-specific spectral information to detect unknowns has received increasing interest. However, affected by noise and interference, high-dimensional hyperspectral anomaly detection is difficult to meet the requirements of high detection accuracy and low false alarm rate. Besides, there is a problem of insufficient and unbalanced samples. To address these problems, we propose a novel hyperspectral anomaly detection framework based on spectral mapping and feature selection (SMFS) in an unsupervised manner. The SMFS introduces the essential properties of hyperspectral data into an unsupervised neural network to construct the nonlinear mapping relationship from high-dimensional spectral space to low-dimensional deep feature space. And it searches the optimal feature subset from the candidate feature space for standing out anomalies. Because of the compelling characterization of the encoder, we develop it specifically for spectral signatures to reveal the hidden data. Quantitative and qualitative experiments on real hyperspectral datasets indicate that the proposed method can provide the compact features overcoming the problems of noise, interference, redundancy and time-consuming caused by high-dimensionality and limited samples. And it has advantages over some state-of-the-art competitors concerning detecting anomalies of different scales.

探索突破未知空间或物质物种的技术对军事和民用领域具有重要意义，而且在没有任何先验信息的情况下，这是一项艰巨的任务。如今，使用特定于材料的光谱信息来检测未知物已引起越来越多的兴趣。然而，受噪声和干扰的影响，高维高光谱异常检测难以满足高检测精度和低虚警率的要求。此外，存在样本不足和不平衡的问题。为了解决这些问题，我们提出了一种基于光谱映射和特征选择（SMFS）的无监督的新型高光谱异常检测框架。SMFS将高光谱数据的基本属性引入到无监督的神经网络中，以构造从高维光谱空间到低维深特征空间的非线性映射关系。然后从候选特征空间中搜索最佳特征子集，以突出异常。由于编码器具有令人信服的特性，因此我们专门针对频谱签名开发了它，以揭示隐藏数据。在真实的高光谱数据集上进行的定性和定量实验表明，该方法可以克服高维和有限采样所带来的噪声，干扰，冗余和费时的问题，提供紧凑的特征。在检测不同规模的异常方面，它比某些最新的竞争对手具有优势。