###### Abstract  
The hyperspectral anomaly detection algorithm based on collaborative representation does not fully utilize the two-dimensional spatial features in hyperspectral images. It also has the problem that anomalous pixels will pollute the background dictionary and induce bad detection performance. Based on these, this paper proposes a hyperspectral anomaly detection algorithm based on multiple feature joint trilateral filtering and collaborative representation. The algorithm first introduces an improved trilateral filtering algorithm, which utilizes the spatial features of hyperspectral images. The preliminary positions of possible abnormal objects are determined. On this basis,  
also proposed correspondingly improved methods in different aspects [4; 5; 6; 7; 8]. [PERSON] et al. effectively combined the kurtosis-driven binary hypothesis test with the RX algorithm to reduce the impact of the local covariance matrix calculation problem in the RX algorithm on the detection accuracy [4]. [PERSON] et al. improved the RX algorithm by introducing an adaptive threshold to separate abnormal objects, which also effectively improved the detection performance [5]. Other improved algorithms with better effects include the fast RX detection algorithm based on heterogeneous clustering [6; 7; 8], the multi-window RX anomaly detection algorithm that uses windows of different sizes to describe different size anomaly objects to solve the problem of window size sensitivity in local anomaly detection [7], and the detection algorithm that further uses RX to effectively fuse spatial spectral features after expanding the multi-attribute profile to extract spatial features [8]. In 2015, the collaborative representation method was applied to the field of hyperspectral image anomaly detection (CRD) for the first time by [PERSON] et al. [9]. Compared with the RX algorithm, this algorithm does not make a Gaussian assumption for the background. It proposes that the background pixel can be approximately represented by the surrounding pixel, while the abnormal pixel cannot. Thus, the center pixel can be reconstructed by using the adjacent pixels. The algorithm uses the sliding double windows to obtain the background dictionary. Thus, the background dictionary is composed of some representative background pixels around the central pixel. The approximate value of the central pixel can be obtained through the linear combination of the background dictionary atoms. The distance between the central pixel and its approximate value is used to determine whether the central pixel is abnormal or not. The CRD algorithm usually shows excellent anomaly detection performance. However, its detection performance is limited to a certain extent because it only considers the spectral features of the hyperspectral images and does not make use of the spatial features of the hyperspectral images. At the same time, there is also the pollution problem of abnormal pixels in the background dictionary. Thus, the algorithm based on collaborative representation and other methods was also optimized to some extent by relevant researchers in later works [10; 11; 12; 13; 14; 15; 16; 17]. The anomaly detection algorithm based on cooperative representation has the problem of insufficient utilization of the spatial features of the hyperspectral images, and the abnormal pixels also cause great interference to the background reconstruction. Therefore, an anomaly detection algorithm based on multi-feature joint trilateral filtering and cooperative representation (MFTFCR) was proposed in this paper. Firstly, an improved trilateral filter was introduced to obtain the abnormal points in the image by utilizing the spatial features of hyperspectral images. Then, the background purification of the hyperspectral image was completed. The purified image data and the original hyperspectral data are used for collaborative representation to complete the reconstruction of the hyperspectral image. Finally, the anomaly detection is completed according to the corresponding reconstruction error.