Ultraspectral Data Compression

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.;This proposal will focus on presenting a whole new architecture for the compression of ultraspectral data presenting sound mathematical models that can be used to describe a set of algorithms and their practical implementation. Specifically we will (1) present a new preprocessing reversible stage that will rearrange the data to make it more efficient when the compression stage is performed; (2) present a new linear prediction based compression stage that will improve the compression rate of any given distortion when compared to literature ultraspectral data compression techniques. This involves defining a distortion measure and its effect on real applications; (3) define a mathematical model to approximate the rate-distortion of the compression stage and compare it against the real performance of the proposed algorithm; (4) evaluate the performance of the overall architecture and algorithms as well as discuss possible optimizations.;To summarize it can be said that the results of this thesis will contribute to the understanding of ultraspectral compression as well as the introduction of a novel multi-platform efficient open-source ultraspectral codec.