Metric-Driven Similarity Indices: Redefining Spectral Distance Comparisons in Hyperspectral Data

Hyperspectral images (HSIs) offer rich spectral details but pose challenges in analyzing spectral vector distances due to high dimensionality and inter-class similarity. Existing distance metrics, while effective in specific cases, often fail to provide consistent comparisons across different tasks due to varying scales. This study proposes novel similarity score indices that normalize metrics onto a unified scale, ensuring fair, interpretable comparisons tailored to the unique properties of HSIs. Our evaluations on public datasets reveal the indices' ability to improve accuracy and reliability in spectral similarity assessments, addressing key challenges in HSI analysis.