CRNSim: A New Similarity Index Capturing Global and Local Spectral Differences in Hyperspectral Data

Abstract

Hyperspectral imaging (HSI) enables detailed spectral analysis across numerous bands, offering transformative potential in diverse domains such as remote sensing, agriculture, and medical diagnostics. However, the inherent challenges of inter-class similarity, intra-class variability, and limitations in existing similarity metrics hinder its effectiveness. To address these challenges, we propose CRNSim, a novel similarity index that integrates three complementary components: a Chebyshev-based term to capture extreme spectral deviations, a RMSE-based term to account for global spectral trends, and a nonlinear adjustment factor to enhance sensitivity to subtle variations while mitigating outlier influence. Experimental evaluations on benchmark hyperspectral datasets, including Indian Pines and Salinas Valley, demonstrate the superiority of CRNSim in improving inter-class separability, outperforming traditional metrics such as Chebyshev and RMSE. These findings highlight CRNSim’s potential to advance HSI analysis methodologies, making it a robust tool for fine-grained spectral differentiation across diverse applications.

Keywords: hyperspectral image analysis, similarity comparison, distance metrics

DOI: 10.54941/ahfe1005923