Hyperspectral Real-Time Local Anomaly Detection Based on Finite Markov via Line-by-Line Processing

Real-time anomaly detection technique can efficiently and effectively leverage available data and operates in tandem with data collection, avoiding dependence on unacquired spectral data. Nonetheless, there are no restrictions or discussions regarding the scope of utilization for existing data. Overloading the analysis with excessive information, particularly encompassing dynamically changing background scenes, can introduce interference, undermining the statistical characteristics of the data and hampering anomaly detection. Studies indicate that local anomaly detection can enhance detection performance. Consequently, determining the optimal scope of the row space within the context of real-time line-by-line processing by integrating local processing and real-time technology stands pivotal in enhancing efficacy. In order to realize real-time hyperspectral local anomaly detection, based on the most widely used push-broom hyperspectral imaging sensor, this article proposes a finite Markov local real-time correlation matrix
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anomaly detection (FMLRT-RAD) by studying the similarity of spectra in adjacent regions of the same substance and the independence of spectra in different regions in hyperspectral images. FMLRT-RAD can adaptively determine the size of the local background region, and solve the challenging task of selecting a suitable data range for local background suppression when an imaging sensor obtains a large amount of data. In addition, based on sample correlation matrix
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anomaly detection, two different correlation matrix representations are designed for dynamically updating finite local samples. Woodbury matrix identity is used to update background suppression, and corresponding update equations with causal recursion characteristics are derived to achieve local real-time anomaly detection to further reduce time consumption and improve detection capability. The experimental results of several real-world hyperspectral image datasets show that the detector has superior detection performance compared with other advanced detectors.