Hyperspectral anomaly detection (HAD) is an important hyperspectral image application. HAD can find pixels with anomalous spectral signatures compared with their neighbor background without any prior information. While most of the existed researches related to statistic-based and distance-based techniques by summarizing the background samples with certain models and then finding the very few outliers by various of distance metrics, this review focuses on the HAD based on machine learning methods, which have witnessed remarkable progress in the recent years. In particular, these studies can generally be roughly grouped into the traditional machine learning and deep learning-based methods. Several representative HAD methods, including both traditional machine and deep learning-based methods, are then conducted on four real HSIs in the experiments. Finally, conclusions regarding HAD are summarized, and prospects and future development direction are discussed.

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