Multiple Spectral Matching Methods Utilized in Hyperspectral Matching

This package contains multiple spectral matching methods essential for hyperspectral analysis: Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), Correlation Angle Mapper (CAM), Gradient Angle Mapper (GAM), Discrete Information Matching (DIM), and Discrete Information Angle Matching (DIAM). These algorithms are crucial in hyperspectral matching as they enable better data comprehension and enhanced information extraction from spectral signatures. The implementation includes vectorized operations for efficient computation of spectral similarities. For instance, Spectral Correlation Mapper calculates cross-correlation coefficients between spectral vectors to identify wavelength dependencies, while Discrete Information Matching employs information theory measures to detect patterns in spectral data distributions. Each method features configurable threshold parameters and normalization options to handle varying data scales. These techniques allow researchers to conduct in-depth hyperspectral data analysis and gain valuable insights. It's important to note that each algorithm has specific strengths and limitations regarding sensitivity to noise, computational complexity, and performance under different illumination conditions. The selection of appropriate matching method should be based on specific application requirements, data characteristics, and desired accuracy levels, with trade-offs considered between computational efficiency and matching precision. The package provides comparative analysis tools to evaluate different methods' performance on given datasets.