Joint optimization of autoencoder and Self-Supervised Classifier: Anomaly detection of strawberries using hyperspectral imaging

Developing unsupervised anomaly detection methods for hyperspectral data is of great importance for its applications in quality and safety control. As a frequently-used anomaly detection method, the autoencoder might suffer from the ineffectiveness of extracting essential representations for distinguishing normal and anomalous samples, since it is only trained to minimize the reconstruction error. To improve the performance of the autoencoder, an anomaly detection method for hyperspectral data named SSC-AE is proposed based on the joint learning of autoencoder and self-supervised classifier, and it is evaluated on the detection of quality defects of strawberries, including bruise, fungal infection, and soil contamination. In the proposed architecture, a self-supervised classification task was designed to discriminate the low-dimensional representations of the normal data and the synthetic anomalous data that extracted from the autoencoder, consequently inducing the autoencoder to learn low-dimensional representations with more discriminative power. Experimental results on hyperspectral data of strawberries show that the SSC-AE demonstrated the best anomaly detection performance and its AUC gains compared with the one-dimensional autoencoder, one-dimensional variational autoencoder, two-dimensional autoencoder, one-class support vector machine and self-supervised classifier achieved 29.0%, 21.2%, 55.5%, 28.1% and 24.9%, respectively. It was also found that the locations and shapes of all three types of strawberry anomalies could be successfully visualized by predicting spectra pixel-by-pixel. Furthermore, the algorithm robustness against impure data in the training procedure was investigated by randomly mixing some anomalous samples into the training set. The SSC-AE degraded gracefully and outperformed all the comparison methods at all impurity levels.