Hyperspectral remote sensing data compression with neural networks

Sebastià Mijares i Verdú, Johannes Ballé, Valero Laparra, Joan Bartrina-Rapesta, Miguel Hernández-Cabronero, Joan Serra-Sagristà

Abstract: Hyperspectral images are typically highly correlated along their spectrum, and this similarity is usually found to cluster in intervals of consecutive bands. We identified 5 such intervals in AVIRIS uncalibrated data (i.e., as captured on-board). These 5 intervals maximised the average spectral correlation along the 224 band spectrum. The resulting in-tervals were composed of bands 1–40, 41–96, 97–155, 156–165, and 166–224, as seen in the figure to the right.