Manifold Learning for Hyperspectral Images

Abstract--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 technique not only preserves the global structure of the data but also enhances feature separability, leading to more accurate and robust classification results. Figure 1: Scheme of the experimental setting (left) view from aside. A top view of the detector is also given (right) to make I. Recent advances in Hyperspectral Images (HSI) analysis have primarily focused on reflection spectroscopy in the visible or near-infrared light domains.