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Applied Vision

Explainable AI for Efficient Hyperspectral Band Selection in Textile Recycling: A Score-CAM Approach

Authors
  • Achraf Guenounou (University of Udine)
  • Harald Ganster (Joanneum Research, Graz)
  • Jean-Philippe Andreu (Joanneum Research)

Abstract

To enable the transition to cost-effective, real-time multispectral sensors, this study introduces a novel Explainable AI (XAI) framework for spectral band selection by adapting Score-CAM—typically used for 2D images—to 1D hyperspectral signals. This XAI-driven approach is rigorously evaluated against established chemometric and machine learning baselines, including Weighted Regression Coefficients (WRC), Variable Importance in Projection (VIP), and the Successive Projections Algorithm (SPA). This method reduces data volume by over 90% while matching full-spectrum baseline performance. Ultimately, this research validates XAI as an interpretable, robust tool for designing efficient, low-cost optical sorting systems for the circular economy.

How to Cite:

Guenounou, A., Ganster, H. & Andreu, J., (2026) “Explainable AI for Efficient Hyperspectral Band Selection in Textile Recycling: A Score-CAM Approach”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 180-188. doi: https://doi.org/10.34749/3061-1466.2026.28

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Published on
2026-06-25

Peer Reviewed

License

Creative Commons Attribution 4.0