Recent studies try to use hyperspectral imaging (HSI) to detect foreign matters in products because it enables to visualize the invisible wavelengths including ultraviolet and infrared. Considering the enormous image channels of the HSI, several dimension reduction methods-e.g., PCA or UMAP-can be considered to reduce but those cannot ease the fundamental limitations, as follows: (1) latency of HSI capturing. (2) less explanation ability of the important channels. In this paper, to circumvent the aforementioned methods, one of the ways to channel reduction, on anomaly detection proposed HSI. Different from feature extraction methods (i.e., PCA or UMAP), feature selection can sort the feature by impact and show better explainability so we might redesign the task-optimized and cost-effective spectroscopic camera. Via the extensive experiment results with synthesized MVTec AD dataset, we confirm that the feature selection method shows 6.90x faster at the inference phase compared with feature extraction-based approaches while preserving anomaly detection performance. Ultimately, we conclude the advantage of feature selection which is effective yet fast.

翻译：近期研究尝试利用高光谱成像检测产品中的异物，因其能够可视化包括紫外线和红外线在内的不可见波段。考虑到高光谱成像具有海量图像通道，可采用主成分分析或统一流形逼近与投影等降维方法来缩减通道数量，但这些方法无法解决以下根本性限制：(1) 高光谱成像捕获的延迟性；(2) 关键通道的可解释性不足。为规避上述方法，本文提出一种通道缩减策略用于高光谱成像异常检测。与特征提取方法（如主成分分析、统一流形逼近与投影）不同，特征选择能够按影响程度对特征进行排序并展现更强的可解释性，因此我们可据此重新设计任务优化且经济高效的光谱相机。通过在合成MVTec AD数据集上的大量实验，我们证实相较于基于特征提取的方法，特征选择方法在推理阶段速度提升6.90倍，同时保持异常检测性能。最终，我们得出特征选择方法兼具高效性与快速性的优势结论。