3.4. Summary

• Each material has a unique spectral fingerprint.
• Spectral libraries (field, lab, or satellite-based) enable material identification and comparison.
• Hyperspectral data is complex and has many redundant/noisy bands.
• Dimensionality reduction methods, like PCA, MNF, and t-SNE compress data while preserving key spectral information.
• Classification & Target Detection
  • Supervised: Uses labelled data for precise mapping (e.g., SVM, Random Forest, SAM).
  • Unsupervised: Groups pixels automatically for exploration (e.g., K-Means, ISODATA).
  • Target detection methods (MF, ACE, RX) identify rare or subtle materials, even sub-pixel.

Spectral analysis transforms raw hyperspectral cubes into actionable insights by reducing complexity, enhancing interpretability, and enabling precise detection of materials and anomalies.