The Infrared Atmospheric Sounding Interferometer (IASI) is flying on board of the Metop satellite series, which is part of the EUMETSAT Polar System (EPS). Products obtained from IASI data represent a significant improvement in the accuracy and quality of the measurements used for meteorological models. Notably, IASI collects rich spectral information to derive temperature and moisture profiles-among other relevant trace gases-, essential for atmospheric forecasts and for the understanding of weather. Here, we investigate the impact of near-lossless and lossy compression on IASI L1C data when statistical retrieval algorithms are later applied. We search for those compression ratios that yield a positive impact on the accuracy of the statistical retrievals. The compression techniques help reduce certain amount of noise on the original data and, at the same time, incorporate spatial-spectral feature relations in an indirect way without increasing the computational complexity. We observed that compressing images, at relatively low bitrates, improves results in predicting temperature and dew point temperature, and we advocate that some amount of compression prior to model inversion is beneficial. This research can benefit the development of current and upcoming retrieval chains in infrared sounding and hyperspectral sensors.

In this paper we analyze the effect of spatial and spectral compression on the performance of statistically based retrieval. Although the quality of the information is not completely preserved during the coding process, experiments reveal that a certain amount of compression may yield a positive impact on the accuracy of retrievals. We unveil two strategies, both with interesting benefits: either to apply a very high compression, which still maintains the same retrieval performance as that obtained for uncompressed data; or to apply a moderate to high compression, which improves the performance. As a second contribution of this work, we focus on the origins of these benefits. On the one hand, we show that a certain amount of noise is removed during the compression stage, which benefits the retrievals performance. On the other hand, we analyze the effect of compression on spectral/spatial regularization (smoothing). We quantify the amount of information shared among the spatial neighbours for the different methods and compression ratios. We also propose a simple strategy to specifically exploit spectral and spatial relations and find that, when these relations are taken into account beforehand, the benefits of compression are reduced. These experiments suggest that compression can be understood as an indirect way to regularize the data and exploit spatial neighbours information, which improves the performance of pixel-wise statistics based retrieval algorithms.

The Infrared Atmospheric Sounding Interferometer (IASI), implemented on the MetOp satellite series, represents a significant step forward in atmospheric forecast and weather understanding. The instrument provides infrared soundings of unprecedented accuracy and spectral resolution to derive humidity and atmospheric temperature profiles, as well as some of the chemical components playing a key role in climate monitoring. IASI collects rich spectral information, which results in large amounts of data (about 16 Gigabytes per day). Efficient compression techniques are requested for both transmission and storage of such huge data. This study reviews the performance of several state of the art coding standards and techniques for IASI L1C data compression. Discussion embraces lossless, near-lossless and lossy compression. Several spectral transforms, essential to achieve improved coding performance due to the high spectral redundancy inherent to IASI products, are also discussed. Illustrative results are reported for a set of 96 IASI L1C orbits acquired over a full year (4 orbits per month for each IASI-A and IASI-B from July 2013 to June 2014) . Further, this survey provides organized data and facts to assist future research and the atmospheric scientific community.

Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it impacts later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from several instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios. In some scenarios, accurate segmentation performance can be achieved even for high compression ratios.

The Infrared Atmospheric Sounding Interferometer (IASI) system provides infrared soundings of moisture and temperature profiles, as well as soundings of chemical components. These measurements play a key role in atmospheric chemistry, global change, and climate monitoring. The instrument, developed by a cooperating agreement between European Organisation for the Exploitation of Meteorological Satellites and Centre National d'Études Spatiales, is implemented on the Metop satellite series. The instrument data production rate is 45 Mb∕s while the transmission rate allocated to IASI measurements is 1.5 Mb∕s. It is thus necessary to implement a significant part of the IASI data processing on-board the instrument. We investigate the information statistics of IASI L0 data once the on-board processing chain is finished. We analyze order-0 entropy, and order-1, order-2 and order-3 conditional entropies, where conditional entropies assess both the spectral and the spatial joint information. According to the simple order-0 entropy, at least one bit per sample could be spared if a variable-length code was employed. We also investigate the actual performance of different lossless compression techniques on IASI L0 data. The CCSDS-123, JPEG-LS, and JPEG2000 standards, as well as M-CALIC coding technique are evaluated. Experimental results reveal that IASI Level 0 data can be coded by a compression ratio above 2.6:1.