Hyperspectral remote sensing collects data in hundreds of narrow, contiguous spectral bands (typically 5–10 nm wide) rather than a handful of broad ones. This produces a full reflectance spectrum for every pixel, which can be matched against lab spectra to identify specific minerals, vegetation species, and pollutants.

• Example sensor: Hyperion on EO-1 — 220 bands, 0.4–2.5 µm, 30 m pixels, 705 km orbit.
• Trade-off: massive data volume and low SNR per band; redundancy usually reduced via PCA or minimum-noise-fraction (MNF) before classification.