Robust compression of multispectral remote sensing data
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2005
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| Online Access: | http://hdl.handle.net/1721.1/9338 |
| _version_ | 1811078772327186432 |
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| author | Cabrera-Mercader, Carlos R. (Carlos Rubén) |
| author2 | David H. Staelin. |
| author_facet | David H. Staelin. Cabrera-Mercader, Carlos R. (Carlos Rubén) |
| author_sort | Cabrera-Mercader, Carlos R. (Carlos Rubén) |
| collection | MIT |
| description | Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999. |
| first_indexed | 2024-09-23T11:05:14Z |
| format | Thesis |
| id | mit-1721.1/9338 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:05:14Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/93382019-04-10T22:24:08Z Robust compression of multispectral remote sensing data Cabrera-Mercader, Carlos R. (Carlos Rubén) David H. Staelin. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999. Includes bibliographical references (p. 241-246). This thesis develops efficient and robust non-reversible coding algorithms for multispectral remote sensing data. Although many efficient non-reversible coding algorithms have been proposed for such data, their application is often limited due to the risk of excessively degrading the data if, for example, changes in sensor characteristics and atmospheric/surface statistics occur. On the other hand, reversible coding algorithms are inherently robust to variable conditions but they provide only limited compression when applied to data from most modern remote sensors. The algorithms developed in this work achieve high data compression by preserving only data variations containing information about the ideal, noiseless spectrum, and by exploiting inter-channel correlations in the data. The algorithms operate on calibrated data modeled as the sum of the ideal spectrum, and an independent noise component due to sensor noise, calibration error, and, possibly, impulsive noise. Coding algorithms are developed for data with and without impulsive noise. In both cases an estimate of the ideal spectrum is computed first, and then that estimate is coded efficiently. This estimator coder structure is implemented mainly using data-dependent matrix operators and scalar quantization. Both coding algorithms are robust to slow instrument drift, addressed by appropriate calibration, and outlier channels. The outliers are preserved by separately coding the noise estimates in addition to the signal estimates so that they may be reconstructed at the original resolution. In addition, for data free of impulsive noise the coding algorithm adapts to changes in the second-order statistics of the data by estimating those statistics from each block of data to be coded. The coding algorithms were tested on data simulated for the NASA 2378-channel Atmospheric Infrared Sounder (AIRS). Near-lossless compression ratios of up to 32:1 (0.4 bits/pixel/channel) were obtained in the absence of impulsive noise, without preserving outliers, and assuming the nominal noise covariance. An average noise variance reduction of 12-14 dB was obtained simultaneously for data blocks of 2400-7200 spectra. Preserving outlier channels for which the noise estimates exceed three times the estimated noise rms value would require no more than 0.08 bits/pixel/channel provided the outliers arise from the assumed noise distribution. If contaminant outliers occurred, higher bit rates would be required. Similar performance was obtained for spectra corrupted by few impulses. by Carlos R. Cabrera-Mercader. Ph.D. 2005-08-22T20:25:56Z 2005-08-22T20:25:56Z 1999 1999 Thesis http://hdl.handle.net/1721.1/9338 44274061 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 246 p. 16777101 bytes 16776859 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Cabrera-Mercader, Carlos R. (Carlos Rubén) Robust compression of multispectral remote sensing data |
| title | Robust compression of multispectral remote sensing data |
| title_full | Robust compression of multispectral remote sensing data |
| title_fullStr | Robust compression of multispectral remote sensing data |
| title_full_unstemmed | Robust compression of multispectral remote sensing data |
| title_short | Robust compression of multispectral remote sensing data |
| title_sort | robust compression of multispectral remote sensing data |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/9338 |
| work_keys_str_mv | AT cabreramercadercarlosrcarlosruben robustcompressionofmultispectralremotesensingdata |