Dimensionality reduction for AI based hyperspectral image classification based on XAI

2026-09-16

AI Summary

研究利用基于AI的降维方法解决木材回收过程中材料回收率低的问题，通过CNN处理高光谱图像，并采用XAI方法提高系统的可解释性。

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.

Problem

• dimensionality reduction
• AI
• hyperspectral image classification
• wood recycling
• XAI

Innovation

• dimensionality reduction
• hyperspectral imaging
• convolutional neural networks
• explainable AI (XAI)
• wood recycling