Hyperspectral real-time detection method based on recursive analysis

Abstract

The invention relates to a hyperspectral real-time detection method based on recursive analysis. The method is characterized by including 1, establishing a spectral vector related matrix R(n) of hyperspectral image pixels; 2, establishing a state equation for the related matrix R(n), and updating an estimated value of current state according to an observed value rn of current state and an estimated value R(n-1) of previous state; 3, updating an inverse matrix R(n)-1 of the related matrix R(n) by a Woodbury identical equation; 4, detecting a hyperspectral image in real time by anomaly detection operators. In the state equation, R(n-1) refers an estimated value of the previous state, and rn refers to an observed value of the current state.

Description

technical field [0001] The invention relates to a hyperspectral real-time detection method based on recursive analysis. Background technique [0002] The emergence of hyperspectral remote sensing is a revolution in remote sensing technology. With the improvement of spectral resolution, it enables the detection of ground objects that cannot be effectively detected in multispectral remote sensing, so it has been widely used. In many practical situations, researchers always do not have enough prior knowledge to characterize the statistics of target classes, such as special species in agronomy, abnormal migration in ecology, rare minerals in geology, toxic waste in environmental monitoring Excretion and oil spills, vehicles or aircraft in the battlefield, cancer cells or tumors in medical diagnosis, etc. Therefore, anomaly detection in hyperspectral images without prior knowledge has received increasing attention. [0003] In actual anomaly detection, real-time detection is p...

AI Technical Summary

Problems solved by technology

However, the existing detection methods use first-order and second-order statistical properties, such as sampling mean and covariance matrix, to design hyperspectral anomaly detection operators, and calculating these statistical properties requires complete hyperspectral data information, so it does not Capable of real-time detection

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