Spectral Parameters Selection Based on Multiagent Deep Reinforcement Learning for AOTF Infrared Multispectral Imaging System

The infrared multispectral imaging system based on acousto-optic tunable filter (AOTF) possesses strong capabilities in suppressing complex background and anti-interferences for detection of aerial targets. It is worth noting that the wavelengths and bandwidths of the detection bands have a significant impact on detection capability. However, the existing spectral parameters selection methods have poor generalization, rely on manually reasoning strategies and repeated training with a large amount of data, cannot achieve the optimum for the spectral parameters. To address these problems, we propose the Wavelength and Bandwidth Selection Model Based on Multi-Agent Reinforcement Deep Learning (WBMARL). This is the first attempt to apply the multi-agent reinforcement learningto spectral parameters selection tasks, which provides a new paradigm. Based on the assumption that the combinatorial optimization problem of spectral parameters selection is regarded as a Markov game process, the WBMARL which consists of a deep feature extraction module and two agents is designed. Through cooperation among the agents, the Nash equilibrium point is reached and the optimal spectral parameters selection policy is obtained. The experimental results on the infrared multispectral images captured by AOTF system indicate that the spectral parameters selected by WBMARL are more conducive to the detection of targets in complex backgrounds and the anti-interference.