用流形学习提升高光谱图像分类效果
Manifold Learning for Hyperspectral Images
- 构建邻接图逼近数据拓扑结构
- 显著提升高光谱图像分类准确率
- 适合处理高维非线性医学成像数据
传统特征提取与投影方法（如主成分分析）难以充分表示X射线透射多能量（XRT ME）图像，限制了神经网络在决策中的表现。为解决此问题，我们提出一种方法，通过均匀流形逼近与投影（UMAP）构建邻接图以近似数据集拓扑。该方法捕捉数据内部的非线性相关性，显著提升机器学习算法性能，尤其适用于从X射线透射光谱获取的高光谱图像（HSI）处理。该技术不仅保留数据全局结构，还增强特征可分性，实现更准确、鲁棒的分类结果。
原文摘要 · Abstract (English)
Traditional feature extraction and projection techniques, such as Principal Component Analysis, struggle to adequately represent X-Ray Transmission (XRT) Multi-Energy (ME) images, limiting the performance of neural networks in decision-making processes. To address this issue, we propose a method that approximates the dataset topology by constructing adjacency graphs using the Uniform Manifold Approximation and Projection. This approach captures nonlinear correlations within the data, significantly improving the performance of machine learning algorithms, particularly in processing Hyperspectral Images (HSI) from X-ray transmission spectroscopy. This technique not only preserves the global structure of the data but also enhances feature separability, leading to more accurate and robust classification results.
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