Spatial Anomaly Detection in Hyperspectral Imaging Using Optical Neural Networks

Abstract

Hyperspectral imaging is a widely used technology, yet hard to implement in real-time anomaly detection due to its extensive data flow volume. An auto-encoder structured hybrid optical-electrical neural network method is proposed in this work that realizes feature exaction and anomaly detection during the hyperspectral data acquisition process to address such issues. In the proposed method, a digital micromirror device functions as the core optical processor to extract low-level features from the hyperspectral data flow. Weight binarization and a conditional sub-network are utilized to suit optical computation. Pretraining by artificial data is implemented to ease the training data burden. Case studies on defect detection and foreign object detection have demonstrated that the proposed method can significantly reduce the sampling time by orders of magnitude without loss of detection accuracy.