Hyperspectral Real-Time Online Processing Local Anomaly Detection via Multiline Multiband Progressing

Anomaly detection, as one of the most critical tasks in hyperspectral image (HSI) processing, has been paid extensive attention in the past decades. The classical work of hyperspectral anomaly detection, sample correlation matrix R-based anomaly detection (R-AD), can achieve desirable detection accuracy, but its processing complexity remains a challenging problem. Real-time anomaly detection methods speed up the procedure through processing the data acquired by grating splitting or acousto-optic tunable filter (AOTF)-based imaging systems in the manner of pixel-by-pixel, line-by-line, or band-by-band. However, in practical industrial scenarios, the multiarray filter spectral imaging system is generally leveraged to avoid high construction and maintenance costs, which acquires multiline multiband (MLMB) data lacking a real-time processing version. In addition, the background of industrial assembly lines is chaotic, and the changes between batches are rapid, both increasing the difficulty of accurate anomaly detection online. To cope with these challenges, a real-time online processing version [real-time multiline multiband R anomaly detection (RTMLMB-RAD)] of R-AD based on MLMB data is proposed here for the first time. Specifically, a multiline multiband correlation matrix (MLMBCM) is designed to update the detector of R-AD recursively with MLMB data acquisition. Since MLMBCM relies only on the previous result and the current data, a large amount of data storage and complex matrix inversion calculation are avoided. At the same time, local anomaly detection mode is adopted to improve the sensitivity of anomalies in different batches and different types of products. The experimental results on six public hyperspectral datasets demonstrate that the proposed RTMLMB-RAD algorithm outperforms other state-of-the-art methods in terms of time cost and detection accuracy. Particularly, the proposed algorithm was tested on the tobacco primary processing line with a detection rate of 87% and a false alarm rate of 10% at a speed of 3 m/s, further verifying the algorithm’s feasibility and effectiveness in practical industrial scenarios.