高光谱成像与化学计量学方法相结合已被证明是水果质量评估和控制的有力工具。在水果缺陷检测场景中,开发一个无监督的异常检测框架是至关重要的,由于缺陷样品制备是劳动密集型和耗时的,特别是探索潜在的缺陷。在本文中,光谱空间,基于信息,提出了一种自监督异常检测(SSAD)方法。在培训期间,提出了一种辅助分类器来识别从高光谱数据立方体转换的主成分(PC)图像的投影轴。在测试时间,学习分类器的完全连接层被用作“频谱空间”特征提取器,并采用特征相似度度量作为下游异常评估任务的评分函数。用两个水果数据集对拟议的网络进行了评估:一个草莓数据集,感染,寒冷受伤,和受污染的测试样本和受伤的蓝莓数据集,感染,寒冷受伤,和起皱的样本作为异常。结果表明,与基线方法相比,SSAD产生了最佳的异常检测性能(平均AUC=0.923)。可视化结果进一步证实了其在提取有效的“光谱空间”潜在表示方面的优势。此外,通过数据污染实验验证了SSAD的鲁棒性;当一部分异常样本参与训练过程时,它的表现明显优于基线。
Hyperspectral imaging combined with chemometric approaches is proven to be a powerful tool for the quality evaluation and control of fruits. In fruit defect-detection scenarios, developing an unsupervised anomaly detection framework is vital, as defect sample preparation is labor-intensive and time-consuming, especially for exploring potential defects. In this paper, a spectral-spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed. During training, an auxiliary classifier is proposed to identify the projection axes of principal component (PC) images that were transformed from the hyperspectral data cubes. In test time, the fully connected layer of the learned classifier was used as a \'spectral-spatial\' feature extractor, and the feature similarity metric was adopted as the score function for the downstream anomaly evaluation task. The proposed network was evaluated with two fruit data sets: a strawberry data set with bruised, infected, chilling-injured, and contaminated test samples and a blueberry data set with bruised, infected, chilling-injured, and wrinkled samples as anomalies. The results show that the SSAD yielded the best anomaly detection performance (AUC = 0.923 on average) over the baseline methods, and the visualization results further confirmed its advantage in extracting effective \'spectral-spatial\' latent representation. Moreover, the robustness of SSAD is verified with the data pollution experiment; it performed significantly better than the baselines when a portion of anomalous samples was involved in the training process.