1 September 2002 Noise-constrained hyperspectral data compression
Author Affiliations +
Optical Engineering, 41(9), (2002). doi:10.1117/1.1496110
Abstract
Storage and transmission requirements for hyperspectral data sets are significant. To reduce hardware costs, well-designed compression techniques are needed to preserve information content while maximizing compression ratios. Lossless compression techniques maintain data integrity, but yield small compression ratios. We present a slightly lossy compression algorithm that uses the noise statistics of the data to preserve information content while maximizing compression ratios. The adaptive principal components analysis (APCA) algorithm uses noise statistics to determine the number of significant principal components and selects only those that are required to represent each pixel to within the noise level. We demonstrate the effectiveness of these methods with airborne visible/infrared spectrometer (AVIRIS), hyperspectral digital imagery collection experiment (HYDICE), hyperspectral mapper (HYMAP), and Hyperion datasets.
Mary H. Sharp, "Noise-constrained hyperspectral data compression," Optical Engineering 41(9), (1 September 2002). http://dx.doi.org/10.1117/1.1496110
JOURNAL ARTICLE
4 PAGES


SHARE
KEYWORDS
Signal to noise ratio

Data compression

Image compression

Interference (communication)

Filtering (signal processing)

Principal component analysis

Detection and tracking algorithms

RELATED CONTENT

Robust 3D DFT video watermarking
Proceedings of SPIE (April 09 1999)
Development of fiber sensors for structural monitoring
Proceedings of SPIE (May 19 1995)
High-contrast imaging with METIS
Proceedings of SPIE (August 09 2016)
Advanced control of low order modes in laser guide star...
Proceedings of SPIE (September 13 2012)

Back to Top