
Evaluation of onboard hyperspectral-image compression techniques for a parallel push-broom sensor
Author(s) -
S Briles
Publication year - 1996
Language(s) - English
Resource type - Reports
DOI - 10.2172/212527
Subject(s) - hyperspectral imaging , row , computer science , data compression , mean squared error , computer vision , full spectral imaging , artificial intelligence , wavelet , remote sensing , mathematics , geography , statistics , database
A single hyperspectral imaging sensor can produce frames with spatially-continuous rows of differing, but adjacent, spectral wavelength. If the frame sample-rate of the sensor is such that subsequent hyperspectral frames are spatially shifted by one row, then the sensor can be thought of as a parallel (in wavelength) push-broom sensor. An examination of data compression techniques for such a sensor is presented. The compression techniques are intended to be implemented onboard a space-based platform and to have implementation speeds that match the date rate of the sensor. Data partitions examined extend from individually operating on a single hyperspectral frame to operating on a data cube comprising the two spatial axes and the spectral axis. Compression algorithms investigated utilize JPEG-based image compression, wavelet-based compression and differential pulse code modulation. Algorithm performance is quantitatively presented in terms of root-mean-squared error and root-mean-squared correlation coefficient error. Implementation issues are considered in algorithm development