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Related Concept Videos

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

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There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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Related Experiment Video

Updated: Feb 28, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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Hyperspectral IASI L1C Data Compression.

Joaquín García-Sobrino1, Joan Serra-Sagristà2, Joan Bartrina-Rapesta3

  • 1Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain. joaquin.garcia.sobrino@deic.uab.cat.

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|June 17, 2017
PubMed
Summary

Efficient compression techniques are crucial for handling large datasets from the Infrared Atmospheric Sounding Interferometer (IASI). This study evaluates various coding standards and spectral transforms for IASI L1C data, aiding atmospheric research.

Keywords:
IASI instrumentdata compressionhyperspectral remote sensinglosslessnear-lossless and lossy compression

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Area of Science:

  • Atmospheric science and remote sensing.
  • Climate monitoring and weather forecasting.

Background:

  • The Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp satellite series provides high-accuracy atmospheric data.
  • IASI generates substantial data volumes (16 GB/day), necessitating efficient data handling for transmission and storage.

Purpose of the Study:

  • To review and assess state-of-the-art compression techniques for IASI L1C data.
  • To evaluate the performance of various coding standards and spectral transforms for data compression.

Main Methods:

  • Analysis of lossless, near-lossless, and lossy compression methods.
  • Investigation of spectral transforms to exploit high spectral redundancy in IASI data.
  • Testing compression techniques on a dataset of 96 IASI L1C orbits over one year.

Main Results:

  • Performance evaluation of different compression techniques for IASI L1C data.
  • Demonstration of improved coding performance through spectral transforms.
  • Provision of organized data and facts for future research.

Conclusions:

  • Effective compression strategies are vital for managing IASI's large datasets.
  • The study provides valuable insights into compression techniques for atmospheric data, supporting the scientific community.