Preserving radiometric resolution in remotely sensed data with lossy compression

Tilton, James C.; Mareboyana, Manohar

doi:10.1109/36.628784

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…The time values reported in Table I, are congruent with (4) and (5). As a tendency, is much larger than any of the other times, being comprised between 80 and 90% of , in customized tests (five to six iterations, four to 16 clusters).…”

Section: Computing Timessupporting

confidence: 78%

“…At the decoder [see Fig. 2(b)] the unitary processing time can be modeled as where (5) in which the time to calculate memberships, , has been approximated as one iteration of the clustering algorithm. The fuzzy prediction at the decoder is identical to that at the encoder; hence, the same appears both in (4) and (5).…”

Section: Computing Timesmentioning

confidence: 99%

“…The latter affects also the spatial quality, thus leading to much larger compressions. For compression ratios greater than, say, four or five, lossy JPEG achieves a better tradeoff between spatial and radiometric quality preservation than LSB truncation does; it is, therefore, preferable on the whole [5]. Nevertheless, quality assessment of lossy compressed data are, obviously, application-dependent, and thus questionable in a universal context.…”

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confidence: 99%

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Lossless compression of multi/hyper-spectral imagery based on a 3-D fuzzy prediction

Aiazzi

Alba²,

Alparone³

et al. 1999

IEEE Trans. Geosci. Remote Sensing

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This paper describes an original application of fuzzy logic to the reversible compression of multispectral data. The method consists of a space-spectral varying prediction followed by context-based classification and arithmetic coding of the outcome residuals. Prediction of a pixel to be encoded is obtained from the fuzzy-switching of a set of linear regression predictors. Pixels both on the current band and on previously encoded bands may be used to define a causal neighborhood. The coefficients of each predictor are calculated so as to minimize the mean-squared error for those pixels whose intensity level patterns lying on the causal neighborhood, belong in a fuzzy sense to a predefined cluster. The size and shape of the causal neighborhood, as well as the number of predictors to be switched, may be chosen by the user and determine the tradeoff between coding performances and computational cost. The method exhibits impressive results, thanks to the skill of predictors in fitting multispectral data patterns, regardless of differences in sensor responses.

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Section: Computing Timessupporting

confidence: 78%

Section: Computing Timesmentioning

confidence: 99%

mentioning

confidence: 99%

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Lossless compression of multi/hyper-spectral imagery based on a 3-D fuzzy prediction

Aiazzi

Alba²,

Alparone³

et al. 1999

IEEE Trans. Geosci. Remote Sensing

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“…A novel approach to improve SAR performance is described, which is a new application of the lifting scheme to generate integer wavelet packets. An interesting comparison between two types of lossy compression applied to remote sensing data appears in [5]. Truncating followed by lossless compression (TLLC) is compared to lossy JPEG compression.…”

Section: Literature Surveymentioning

confidence: 99%

Application specific compression : final report.

Melgaard¹,

Byrne²,

Myers³

et al. 2008

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With the continuing development of more capable data gathering sensors, comes an increased demand on the bandwidth for transmitting larger quantities of data. To help counteract that trend, a study was undertaken to determine appropriate lossy data compression strategies for minimizing their impact on target detection and characterization. The survey of current compression techniques led us to the conclusion that wavelet compression was well suited for this purpose. Wavelet analysis essentially applies a low-pass and high-pass filter to the data, converting the data into the related coefficients that maintain spatial information as well as frequency information. Wavelet compression is achieved by zeroing the coefficients that pertain to the noise in the signal, i.e. the high frequency, low amplitude portion. This approach is well suited for our goal because it reduces the noise in the signal with only minimal impact on the larger, lower frequency target signatures. The resulting coefficients can then be encoded using lossless techniques with higher compression levels because of the lower entropy and significant number of zeros. No significant signal degradation or difficulties in target characterization or detection were observed or measured when wavelet compression was applied to simulated and real data, even when over 80% of the coefficients were zeroed. While the exact level of compression will be data set dependent, for the data sets we studied, compression factors over 10 were found to be satisfactory where conventional lossless techniques achieved levels of less than 3. 5

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Implementing an end-to-end integration of Earth system science in post-secondary education

Mareboyana

Wakim²,

Lawrence³

IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293)

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Preserving radiometric resolution in remotely sensed data with lossy compression

Cited by 3 publications

References 9 publications

Lossless compression of multi/hyper-spectral imagery based on a 3-D fuzzy prediction

Lossless compression of multi/hyper-spectral imagery based on a 3-D fuzzy prediction

Application specific compression : final report.

Implementing an end-to-end integration of Earth system science in post-secondary education

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