ExplainabilityTransparencyAccountabilitycs.CV
Dimensionality reduction for AI based hyperspectral image classification based on XAI
Vladimir Zeljković, Branka Stojanović, Harald Ganster, Aleksandar Nešković
September 16, 2026
Key Takeaways
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AI can improve recycling efficiency: The research demonstrates that AI-based hyperspectral imaging with over 200 spectral channels can enhance wood recycling processes, suggesting practical applications where AI technology directly supports sustainability goals.
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Explainability is essential for industrial AI: The paper emphasizes explainable AI (XAI) methods as a core component of their framework, indicating that for AI systems to be trusted and effectively deployed in real-world recycling operations, stakeholders need to understand how the AI makes its decisions.
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Dimensionality reduction balances performance and practicality: By streamlining feature space for AI training and inference, the research addresses a key challenge in responsible AI deployment—making systems more efficient and interpretable without sacrificing accuracy, which is crucial for widespread adoption in industry.
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Responsible AI requires domain-specific solutions: The focus on wood recycling demonstrates that effective AI implementation in specialized fields requires tailored approaches that combine technical sophistication (CNNs, hyperspectral imaging) with explainability, rather than applying generic AI solutions.
Abstract
This research addresses the challenge of limited material recycling in wood recycling processes by leveraging artificial intelligence (AI)-based dimensionality reduction. Our study explores the application of convolutional neural networks (CNNs) in multi-channel hyperspectral imaging (HSI), extending beyond RGB channels to over 200 spectral channels. Dimensionality reduction within this context involves streamlining the feature space for AI system training and inference. Focusing on explainable AI (XAI) methods, this paper contributes to a broader research initiative, presenting a solution framework that enhances the sustainability and efficiency of wood recycling processes.