TY - CHAP
T1 - Hyperspectral remote sensing data compression with neural networks
AU - Verdú, Sebastià Mijares I.
AU - Balle, Johannes
AU - Laparra, Valero
AU - Rapesta, Joan Bartrina
AU - Hernandez-Cabronero, Miguel
AU - Serra-Sagristá, Joan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85134419864
U2 - 10.1109/DCC52660.2022.00087
DO - 10.1109/DCC52660.2022.00087
M3 - Chapter
AN - SCOPUS:85134419864
T3 - Data Compression Conference Proceedings
SP - 476
BT - Proceedings - DCC 2022
A2 - Bilgin, Ali
A2 - Marcellin, Michael W.
A2 - Serra-Sagrista, Joan
A2 - Storer, James A.
PB - Institute of Electrical and Electronics Engineers Inc.
ER -