Automated Individual-Level ROI-to-Spectrum Extraction for Hyperspectral Analysis in Forensic Entomology

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Abstract
Hyperspectral imaging (HSI) has potential for forensic entomology, but its practical use is limited by manual region-of-interest (ROI) delineation before spectral extraction. This step is time-consuming, operator-dependent, and difficult to standardize across insect species and developmental stages. Here, we developed an automated individual-level ROI-to-spectrum workflow for HSI analysis of forensically important insects. The dataset included 63 hyperspectral images and 1868 manually annotated insect individuals, covering larvae, pupae, and adults. The proposed Hyperspectral Imaging Fully Convolutional Network (HSI-FCN) segmented insect body regions from three-band pseudo-RGB images, back-projected the predicted masks to the original HSI data cubes, generated individual-level ROIs, and extracted full-band mean spectra. On an independent test set containing 204 insect individuals, HSI-FCN achieved mean Dice and intersection over union (IoU) values of 0.9079 and 0.8328, respectively, and showed the best overall performance among representative segmentation models. All test individuals were successfully matched with their corresponding manual ROIs. Spectra extracted from automated ROIs were highly consistent with manual ROI spectra, with a mean spectral angle mapper of 3.06° and a Pearson correlation coefficient of 0.9956. These results show that the proposed workflow can replace manual ROI delineation with a reproducible preprocessing step for insect HSI analysis, supporting standardized spectral extraction and future applications in forensic entomology.
Funding Information
  • China Postdoctoral Science Foundation (2025M781426)

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