Deep Learning-Enhanced Hyperspectral Imaging for Forensic Evidence Analysis: Methodological Frameworks, Applications,...

Area of Science:

• Forensic Science
• Computer Vision
• Spectroscopy

Background:

• Hyperspectral imaging (HSI) provides detailed spatial-spectral data for nondestructive forensic analysis.
• Traditional methods struggle with HSI's high dimensionality and spectral complexity, necessitating advanced techniques.
• Limited forensic sample availability further complicates traditional feature-based analysis.

Purpose of the Study:

• To systematically review the integration of deep learning (DL) and HSI in forensic science.
• To explore DL paradigms like CNNs, attention mechanisms, and GANs for forensic HSI.
• To analyze multimodal integration strategies and evaluate forensic applications of DL-HSI.

Main Methods:

• Review of deep learning paradigms: CNNs, attention mechanisms, and GANs.
• Analysis of multimodal integration strategies (input, feature, decision levels).
• Critical evaluation of forensic applications: trace fibers, inks, soil, documents.

Main Results:

• Deep learning models offer robust data-driven approaches for complex hyperspectral forensic data.
• CNNs excel at spatial-spectral feature extraction; attention mechanisms enhance interpretability; GANs aid data augmentation.
• Multimodal integration strategies show promise for improved analytical performance.

Conclusions:

• Deep learning and HSI integration presents a powerful, data-driven approach for forensic evidence analysis.
• Addressing challenges like interpretability, computational efficiency, and data standardization is crucial for practical deployment.
• Future directions include lightweight architectures, physics-informed learning, and advanced multimodal fusion for defensible forensic analytics.