Dimensionality reduction for AI based hyperspectral image classification based on XAI

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.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2609.22333 [cs.CV]

(or arXiv:2609.22333v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.22333 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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