Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using
  Dimension Reduction Methods
Dongeon Kim YeongHyeon Park
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.