Research Article

Dimensionality reduction method based on spatial-spectral preservation and minimum noise fraction for hyperspectral images

¹ Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang, Hebei 050000, China
² State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang, Henan 471003, China

^* Corresponding author. denglei@aeu.edu.cn

Received: 24 April 2025
Accepted: 9 June 2025

Abstract

Hyperspectral images contain rich spatial distribution and spectral information of land features, but they also introduce high information redundancy and computational complexity. This paper proposes dimensionality reduction methods that integrate spatial-spectral preservation and minimum noise fraction (MNF) to better analyze and utilize the spatial and spectral information in hyperspectral images. While performing the minimum noise separation transformation, the proposed method aims to preserve the spatial structure of the image as much as possible, maximizing both the signal-to-noise ratio and the spatial structure similarity of the image. The component selection strategy involves grouping components and calculating the average change in the relative position of all pixels in the feature space. The component group that most closely matches the spectral relative position before transformation is selected as the final dimensionality reduction result. Experimental results demonstrate that the proposed method is highly sensitive to noise estimation and requires a relatively accurate noise covariance matrix. The method effectively preserves spatial information, with negligible impact on the accuracy of object detection methods, and outperforms other comparative approaches. It ensures the effectiveness of downstream object detection tasks while significantly reducing computational time. The code of the proposed method is available at https://github.com/aosilu/spatial-spectral-preservation-MNF.