Dr. Srikanth Gottipati Profile

Dr. Srikanth Gottipati

at City College of New York

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Author

Publications (8)

Proceedings Article | 3 September 2009 Paper

Error mitigation for CCSD compressed imager data

Irina Gladkova, Michael Grossberg, Srikanth Gottipati, Fazlul Shahriar, George Bonev

Proceedings Volume 7444, 744408 (2009) https://doi.org/10.1117/12.826361

KEYWORDS: Imaging systems, Image restoration, Image compression, Statistical analysis, Infrared imaging, Error analysis, Visible radiation, Sensors, Satellites, Multispectral imaging

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Proceedings Article | 27 April 2009 Paper

A comparative study of lossless compression algorithms on multispectral imager data

Michael Grossberg, Srikanth Gottipati, Irina Gladkova, Malka Rabinowitz, Paul Alabi, Tence George, Amnia Pacheco

Proceedings Volume 7334, 733408 (2009) https://doi.org/10.1117/12.821007

KEYWORDS: MODIS, Image compression, Imaging systems, Satellites, Satellite imaging, Multispectral imaging, Sensors, JPEG2000, Infrared imaging, Algorithm development

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This paper reports a comparative study of current lossless compression algorithms for data from a representative selection of satellite based earth science multispectral imagers. The study includes the performance of compression algorithms on Advanced Very High Resolution Radiometer(AVHRR), SEVIRI, the Moderate Resolution Imaging Spectroradiometer(MODIS) imager, as well as a subset of MODIS bands as a proxy for the upcoming GOES-R series. SEVIRI aboard the ESA/EUMETSAT operated Meteosat Second Generation (MSG) satellites is a geostationary imager. The AVHRR aboard the NOAA Polar Orbiting Environmental Satellites and MODIS aboard the NASA Terra and Aqua satellites have polar orbits. Thus this study will present representatives from both polar and geostationary orbiting imagers. The imagers we include have sensors for both reflected and emissive radiance. We also note that the older satellites have coarser quantizations and present our conclusions on the impact on compression ratios. Faced with a enormous growing large volume of data on a new emerging current generation images from faster scanning, finer spatial resolution, and greater spectral resolution, this study provides a comparison of current compression algorithms as a baseline for future work. With growing satellite Earth science multispectral imager volume data, it becomes increasingly important to evaluate which compression algorithms are most appropriate for data management in transmission and archiving. This comparative compression study uses a wide range standard implementations of the leading lossless compression algorithms. Examples include image compression algorithms such as PNG and JPEG2000, and widely-used file compression formats such as BZIP2 and 7z. This study includes a comparison with the Consultative Committee for Space Data Systems (CCSDS) recommended Szip software which uses the extended-Rice lossless compression algorithm as well as the most recent recommended compression standard which relies on a wavelet transform followed by an entropy coder. To establish statistical significance of our analysis, we have developed a system to acquire and manage a large number of imager granules: currently over 1000 MODIS granules, over 2400 AVHRR granules, and over 220 SEVIRI granules.

Proceedings Article | 5 September 2008 Paper

An analysis of the information dependence between MODIS emissive bands

Srikanth Gottipati, Irina Gladkova, Michael Grossberg

Proceedings Volume 7084, 70840J (2008) https://doi.org/10.1117/12.800817

KEYWORDS: MODIS, Clouds, Image compression, Analytical research, Sensors, Data compression, Data modeling, Matrices, Imaging systems, JPEG2000

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Multispectral, hyperspectral and ultraspectral imagers and sounders are increasingly important for atmospheric science and weather forecasting. The recent advent of multipsectral and hyperspectral sensors measuring radiances in the emissive IR are providing valuable new information. This is due to the presence of spectral channels (in some cases micro-channels) which are carefully positioned in and out of absorption lines of CO2, ozone, and water vapor. These spectral bands are used for measuring surface/cloud temperature, atmospheric temperature, Cirrus clouds water vapor, cloud properties/ozone, and cloud top altidude etc. The complexity of the spectral structure wherein the emissive bands have been selected presents challenges for lossless data compression; these are qualitatively different than the challenges offered by the reflective bands. For a hyperspectral sounder such as AIRS, the large number of channels is the principal contributor to data size. We have shown that methods combining clustering and linear models in the spectral channels can be effective for lossless data compression. However, when the number of emissive channels is relatively small compared to the spatial resolution, such as with the 17 emissive channels of MODIS, such techniques are not effective. In previous work the CCNY-NOAA compression group has reported an algorithm which addresses this case by sequential prediction of the spatial image. While that algorithm demonstrated an improved compression ratio over pure JPEG2000 compression, it underperformed optimal compression ratios estimated from entropy. In order to effectively exploit the redundant information in a progressive prediction scheme we must, determine a sequence of bands in which each band has sufficient mutual information with the next band, so that it predicts it well. We will provide a covariance and mutual information based analysis of the pairwise dependence between the bands and compare this with the qualitative expected dependence suggested by a physical analysis. This compression research is managed by Roger Heymann, PE of OSD NOAA NESDIS Engineering, in collaboration with the NOAA NESDIS STAR Research Office through Mitch Goldberg, Tim Schmit, Walter Wolf.

Proceedings Article | 5 September 2008 Paper

Lossless compression algorithm for multispectral imagers

Irina Gladkova, Michael Grossberg, Srikanth Gottipati

Proceedings Volume 7084, 70840D (2008) https://doi.org/10.1117/12.800819

KEYWORDS: Image compression, Imaging systems, JPEG2000, MODIS, Multispectral imaging, Algorithm development, Computer programming, Satellites, Data archive systems, Satellite imaging

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Multispectral imaging is becoming an increasingly important tool for monitoring the earth and its environment from space borne and airborne platforms. Multispectral imaging data consists of visible and IR measurements from a scene across space and spectrum. Growing data rates resulting from faster scanning and finer spatial and spectral resolution makes compression an increasingly critical tool to reduce data volume for transmission and archiving. Research for NOAA NESDIS has been directed to finding for the characteristics of satellite atmospheric Earth science Imager sensor data what level of Lossless compression ratio can be obtained as well as appropriate types of mathematics and approaches that can lead to approaching this data's entropy level. Conventional lossless do not achieve the theoretical limits for lossless compression on imager data as estimated from the Shannon entropy. In a previous paper, the authors introduce a lossless compression algorithm developed for MODIS as a proxy for future NOAA-NESDIS satellite based Earth science multispectral imagers such as GOES-R. The algorithm is based on capturing spectral correlations using spectral prediction, and spatial correlations with a linear transform encoder. In decompression, the algorithm uses a statistically computed look up table to iteratively predict each channel from a channel decompressed in the previous iteration. In this paper we present a new approach which fundamentally differs from our prior work. In this new approach, instead of having a single predictor for each pair of bands we introduce a piecewise spatially varying predictor which significantly improves the compression results. Our new algorithm also now optimizes the sequence of channels we use for prediction. Our results are evaluated by comparison with a state of the art wavelet based image compression scheme, Jpeg2000. We present results on the 14 channel subset of the MODIS imager, which serves as a proxy for the GOES-R imager. We will also show results of the algorithm for on NOAA AVHRR data and data from SEVIRI. The algorithm is designed to be adapted to the wide range of multispectral imagers and should facilitate distribution of data throughout globally. This compression research is managed by Roger Heymann, PE of OSD NOAA NESDIS Engineering, in collaboration with the NOAA NESDIS STAR Research Office through Mitch Goldberg, Tim Schmit, Walter Wolf.

Proceedings Article | 19 September 2007 Paper

A new lossless compression algorithm for satellite earth science multi-spectral imagers

Irina Gladkova, Srikanth Gottipati, Michael Grossberg

Proceedings Volume 6683, 668307 (2007) https://doi.org/10.1117/12.736584

KEYWORDS: Image compression, Imaging systems, Sensors, MODIS, Satellites, Algorithm development, JPEG2000, Image processing, Satellite imaging, Earth sciences

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Proceedings Article | 19 September 2007 Paper

The impact of striping artifacts on compression

Michael Grossberg, Srikanth Gottipati, Irina Gladkova

Proceedings Volume 6683, 66830L (2007) https://doi.org/10.1117/12.736585

KEYWORDS: Sensors, Image compression, Image sensors, MODIS, Image processing, Calibration, Satellites, Long wavelength infrared, Satellite imaging, Imaging systems

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Proceedings Article | 19 September 2007 Paper

A comparative study of lossless compression algorithms on MODIS data

Srikanth Gottipati, Jamal Goddard, Michael Grossberg, Irina Gladkova

Proceedings Volume 6683, 66830F (2007) https://doi.org/10.1117/12.736771

KEYWORDS: JPEG2000, MODIS, Image compression, Imaging systems, Data archive systems, Satellites, Satellite imaging, Data compression, Reflectivity, Algorithm development

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Proceedings Article | 1 September 2006 Paper

An analysis of optimal compression for the advanced baseline imager based on entropy and noise estimation

M. Grossberg, S. Gottipati, I. Gladkova, M. Goldberg, L. Roytman

Proceedings Volume 6300, 63000M (2006) https://doi.org/10.1117/12.681487

KEYWORDS: Imaging systems, Data modeling, Image compression, Sensors, Statistical analysis, Algorithm development, Expectation maximization algorithms, Data compression, Satellites, Data analysis

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Showing 5 of 8 publications

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