## 1 Introduction  
Generally, the process of classical method of target detection for hyperspectral remote sensing images can be expressed as follows: the spectral features of the image data points are projected onto a given plane by a certain criterion (i.e., detector) to ensure that the target and the backgrounds are located at different positions in this detection result plane, and then the target is separated from the background by a method of threshold segmentation [1]. During the detection process of hyperspectral targets, different models describing the background lead to different classifications of hyperspectral target detection methods. The classical methods of target detection for hyperspectral remote sensing images are divided into two categories: structured and unstructured backgrounds [2]. Three mathematical models exist to describe the spectral variation of pixels in hyperspectral images for structured background target detection: the probability density model, the subspace model, and the linear spectral hybrid model. Apart from the structured background model, a small number of hybrid detectors that combine the advantages of both have been investigated, and have been developed for both hybrid structured and unstructured detectors. Unlike the structured background method, the unstructured background detectors do not adopt this structured model of endmember signals with corresponding components to express background information. Instead, the background is treated as a statistical model conforming to a multivariate Gaussian distribution, and uniformly models the background and noise as the background. Typical methods include Generalized Likelihood Ratio (GLR), Adaptive Coherence Estimator (ACE), and Adaptive Matching Filter (AMF). In recent years, several new methods of pattern recognition and machine learning have emerged in the field of hyperspectral remote sensing image target detection, including the Kernel Method [3], Sparse Representation Model [4], Discriminant Subspace Analysis [5; 6] and Deep Learning Model [7]. For instance, a hyperspectral image anomaly detection method based on Support Vector Data Description (SVDD) was proposed by [PERSON] et al. [8], which can describe the image background depending on a few pixels without requiring the prior knowledge of data distribution. However, the shortcoming of this method mainly lies in the fact that the selection of kernel parameters of the algorithm is too dependent on human experience, and no scientific method of parameter selection is proposed. [PERSON] et al. proposed a Collaborative Representation based Detector (CRD) algorithm for anomaly detection in hyperspectral images [9], assuming that background pixels can be expressed as linear combinations of neighboring pixels, while anomalous pixels do not follow this property, and using this property for anomalous target detection. The algorithm is a non-parametric method that does not require estimation of the background covariance matrix and has a closed solution. Although algorithms such as deep learning have been progressively applied to hyperspectral target extraction, they also suffer from several bottlenecks: Firstly, the establishment of models in deep learning methods often requires certain numbers of reliable training samples, which is generally not satisfied in the field of hyperspectral target detection since the prior knowledge of hyperspectral target detection problem is only a single target spectrum, and the generalization and migration of the model is poor [10]. Secondly, it also possesses numerous parameters for deep learning methods to determine, and there is not enough data for cross-validation of multiple parameters in hyperspectral target detection [11]. Lastly, the computational complexity of machine learning methods tends to increase dramatically along with the expansion of problem scale, and the sample sets in hyperspectral target detection often reach megapixels, making it difficult to meet the requirements in real-time.