Research progress on hyperspectral anomaly detection

The applications of remote sensing images in numerous fields have been increasing with the continuous development of aerospace and remote sensing technologies. HyperSpectral Image (HSI) is a common type of remote sensing image that comprises a series of two-dimensional remote sensing images as a 3D data cube. Each two-dimensional image in HSI can reveal 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 are characterized by“spatial-spectral integration,” which contains not only spectral information with strong discriminant but also rich spatial information. Therefore, the hyperspectral data have considerable application potential. Hyperspectral anomaly detection aims to detect pixels in a scene with different characteristics from surrounding pixels and determines them as anomalous targets without any previous knowledge of the target. Hyperspectral anomaly detection is an unsupervised process that does not require any priori information regarding the target to be measured in advance; thus, this type of detection plays a crucial role in real life. For example, anomaly target detection technology can be used to search and rescue people after a disaster, quickly determine the fire point of a forest fire, and search mineral points 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 numerous researchers have conducted extensive research and achieved rich research results. However, hyperspectral anomaly detection still encounters many difficult problems. For example, the targets of the same material may exhibit various spectral characteristics due to the different imaging equipment and environment, which may interfere with the detection results and lead to the problem of“same object with different spectra.”Meanwhile, the targets of different materials may also exhibit the problem of“different objects with different spectra.”Then, most of the existing hyperspectral anomaly detection algorithms are only in the laboratory stage and with low technology maturity. Furthermore, the hyperspectral data may have numerous spectral bands that contain a considerable amount of redundant information, which increases the difficulty of data processing. Moreover, the number of publicly available hyperspectral anomaly detection datasets is insufficient and mostly old. In this paper, the main research progress of hyperspectral anomaly detection is first summarized. The existing mainstream algorithms are then classified and summarized. These algorithms are mainly divided into five categories: statistics-based anomaly detection methods, data expression-based anomaly detection methods, data decomposition-based anomaly detection methods, deep learning-based anomaly detection methods, and other methods. Through the investigation, analysis, and summary of the existing methods, three future development directions of hyperspectral anomaly detection are proposed. (1) Database expansion: new datasets with additional images and highly sophisticated remote sensing sensors are introduced. (2) Multisource data combination: the advantages of different imaging sensors and various types of remote sensing data are maximized. (3) Algorithm practicality: the anomaly detection algorithms are relayed for application on real platforms. © 2024 Science Press. All rights reserved.