随着航空航天技术与遥感技术的不断发展，遥感影像在诸多领域的应用不断拓展，其中高光谱分辨率遥感影像具有“图谱合一”的特点，即该数据既包含了具有强大区分性的地物光谱信息，又包含了丰富的地物空间位置信息，因此高光谱数据具有非常大的应用潜力。高光谱异常目标检测问题，是在对目标先验信息未知的前提下，根据光谱与空间信息实现对区域中的异常目标的进行“盲”检测，因此其在资源调查、灾害救援等领域发挥了巨大的作用，是遥感领域非常重要的研究课题。本文针对高光谱遥感影像异常目标检测研究方向，首先总结阐述了目前高光谱异常目标检测问题的主要研究进展，根据算法原理的不同对现有主流算法进行了分类与总结，主要分成了基于统计学、基于数据表达、基于数据分解、基于深度学习等不同的种类的方法，并对每类方法的特点进行分析。随后通过对现有方法的调研、分析与总结，提出了数据库拓展、多源数据融合、算法实用化等高光谱异常检测研究未来发展的三个方向。

With the continuous development of aerospace technology and remote sensing technology, the applications of remote sensing images in lots of fields have been expanding. Hyperspectral image (HSI) is a common type of remote sensing image which consists of a series of two-dimensional remote sensing images as a 3D data cube. Each two-dimensional image in HSI can reflect the reflection/radiation intensity of different wavelengths of electromagnetic waves, and each pixel of HSI corresponds to the spectral curve reflecting the spectral information in different wavelengths. Therefore, the hyperspectral remote sensing images have the feature of "map-spectrum integration", which contains not only the spectral information with strong discriminative, but also rich spatial information, so the hyperspectral data have great application potential. Hyperspectral anomaly detection is to detect the pixels in a scene with different characteristics from surrounding pixels and determines them as anomalous targets without the any prior knowledge of the target. Since hyperspectral anomaly detection is an unsupervised process that does not require any priori information about the target to be measured in advance, it plays a great role in real life. For example, anomaly target detection technology can be used to search and rescue people after a disaster, to quickly determine the fire point of a forest fire, and to search mineral point in mineral resource exploration. Hyperspectral anomaly detection has been a popular research direction in the area of remote sensing image processing in recent years and a large number of researchers have conducted extensive and in-depth research and achieved rich research results. However, hyperspectral anomaly detection still faces many difficult problems, such as the targets of the same material may exhibit different spectral characteristics due to the different imaging equipment and imaging environment, which may interfere with the detection results and lead to the problem of "same object with different spectrum", and the targets of different materials may also exhibit the problem of "different object with different spectrum". Then, most of the existing hyperspectral anomaly detection algorithms are only in the laboratory stage and with low technology maturity. Besides, the hyperspectral data may have lots of spectral bands that contains a large amount of redundant information, which makes the data processing difficult. Moreover, the number of publicly available hyperspectral anomaly detection datasets is insufficient and most of the datasets are very old. In this paper, we firstly summarize the main research progress of hyperspectral anomaly detection, and then classify and summarize the existing mainstream algorithms, mainly divided into five categories: statistics-based anomaly detection methods, data expression-based anomaly detection methods, data decomposition-based methods anomaly detection, deep learning-based methods anomaly detection and other methods. Besides, through the investigation, analysis and summary of the existing methods, three future development directions of hyperspectral anomaly detection are proposed, including database expansion: introduce newer dataset with more images and more sophisticated remote sensing sensors, multi-source data combination: take advantages of different imaging sensors and different types of remote sensing data; algorithm practical: enables the anomaly detection algorithms to be ported for application on real platforms.