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Compression Group

Dr. Irina Gladkova
Dr. Leonid Roytman

Marcel Kei
John Weber


Walter Wolf

Mitch Goldberg

Roger Heymann


This project focuses on the development of lossless algorithms for compression of the signals from environmental satellites. Specifically, our group is designing, analyzing, and implementing a compression suite for the next-generation GOES-R instrument to be launched in 2012.

The Problem

Environmental satellites must process up to 20 million samples in one second while operating at less than 1 watt per million samples per second, and environmental scientists are interested in every bit of that data. More importantly, this data must be transmitted over a noisy medium with limited bandwidth.

Atmospheric Infrared Sounder (AIRS)

Our group will be using current spacecraft to simulate data from the upcoming GOES-R instrument. We will be focusing on Aqua Spacecraft's AIRS instrument in our case study. The AIRS is a high resolution instrument which measures infrared radiances at 2378 frequencies ranging from 3.74-15.4 µm. The AIRS takes 90 measurements as it scans 49.5° perpendicular to the satellite's orbit every 2.667 seconds (cross-track). The data is distributed in granules -- 6 minutes of data (or 135 of these scans). So our input is a 90x135x2378 cube of data;

The Solution

Haar/Cosine Transform

We used non-separable, multi-dimensional transforms for data reduction along the natural dimensionality of the sounder data. This immediately improved the compression ratio.

[Haar/Cosine Transform]

Empirical Mode Decomposition

We used a variant of the empirical mode decomposition with an adaptive clustering algorithm in order to smooth the data.

[Empirical Mode Decomposition]