Year: 2012
Type: dissertation
Abstract: Ultraspectral images capture 2D data tuned at different wavelengths across the mid infrared electromagnetic spectrum. Atmospheric Infrared Sounder (AIRS) remote sensors are ultraspectral sensors that generate images with thousands of highly correlated bands and are considered the future of spectroscopy. The major application of AIRS is the acquisition of atmospheric parameters such as gases, temperature and other quantities to perform climate and weather forecast. Because typical AIRS images are well over 40 MB in size and because they are captured in remote locations data compression (before transmission) becomes a very critical issue. Existent compression studies of AIRS data adapt generic multispectral image compression techniques (not necessarily ultraspectral) but do not take into account the particular nature of ultraspectral images. Most of them do not consider correlation beyond one band, use fixed linear prediction (leading to significant distortion) and are not optimized to overcome time and space complexity constraints. Moreover compression studies do not provide analytical models of rate-distortion either.
DOI: 10.17760/d20002874
Author: Rolando Herrero
Institution: Northeastern University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2276955179