研究PCA在多光谱图像多标签分类中的作用，发现其效果依赖模型和训练策略。
Examination of PCA Utilisation for Multilabel Classifier of Multispectral Images
- 用PCA将高维多光谱数据降至三维，再接入三层分类器
- 实验表明PCA效果因深度模型和训练方式而异
- 适合关注多标签分类与降维方法的遥感或计算机视觉研究者
本文研究主成分分析（PCA）在基于ResNet50和DINOv2的多光谱图像多标签分类中的应用，针对此类数据维度高、处理复杂的问题。多标签分类要求每张图像可归属多个类别，进一步增加特征提取难度。本研究流程包含一个可选的PCA步骤，将数据压缩至三维后输入三层分类器。结果表明，PCA在多标签多光谱图像分类中的有效性高度依赖所选深度学习架构与训练策略，为自监督预训练及替代降维方法的研究提供了新方向。
原文摘要 · Abstract (English)
This paper investigates the utility of Principal Component Analysis (PCA) for multi-label classification of multispectral images using ResNet50 and DINOv2, acknowledging the high dimensionality of such data and the associated processing challenges. Multi-label classification, where each image may belong to multiple classes, adds further complexity to feature extraction. Our pipeline includes an optional PCA step that reduces the data to three dimensions before feeding it into a three-layer classifier. The findings demonstrate that the effectiveness of PCA for multi-label multispectral image classification depends strongly on the chosen deep learning architecture and training strategy, opening avenues for future research into self-supervised pre-training and alternative dimensionality reduction approaches.
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