Spectral and spatial decorrelation of Landsat-TM data for lossless compression

Wang, J.; Zhang, Max; Tang, Shouwen

doi:10.1109/36.469492

Cited by 44 publications

(11 citation statements)

References 17 publications

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“…Thus, all the infrared bands are a little correlated, both with the visible bands and with one another. Band 4 is especially so weakly correlated with any other band, that only spatial prediction is usually employed for its decorrelation [7].…”

Section: A Landsat Tm Datamentioning

confidence: 99%

“…To achieve an optimum multispectral decorrelation, the different bands available were arranged to form a sequence that maximizes the average cross-correlation between any couple of consecutive bands [7]. The optimum sequence, which depends mostly on the sensor and to a lesser extent on the cover, was found to be [10], [17] for all the three test TM images.…”

Section: A Landsat Tm Datamentioning

confidence: 99%

“…DPCM basically consists of a spatial prediction followed by entropy coding of the outcome prediction errors. The simplest way to design a predictor, once a causal neighborhood is set, viz., a set of pixels whose intensity levels have previously been encoded according to the image scan fashion, is to take a linear combination, or regression, of the values of such a neighborhood, with coefficients optimized in order to yield minimum mean squared error (MMSE) over the whole image [7]. The major inconvenience of this approach is that such a prediction is optimum only for stationary signals.…”

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confidence: 98%

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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: A Landsat Tm Datamentioning

confidence: 99%

Section: A Landsat Tm Datamentioning

confidence: 99%

mentioning

confidence: 98%

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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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show abstract

“…Thus, compression methods for multispectral images must be developed. There exist both lossless [3][4][5] and lossy schemes [6][7][8]. The compression ratios in lossless compression are from 2 to 5, but in lossy compression they can be ten-fold.…”

Section: Introductionmentioning

confidence: 99%

“…The lossless methods applied vector quantisation [3], prediction and band ordering [4], and spectral and spatial decorrelation with residual image coding [5].…”

Section: Introductionmentioning

confidence: 99%

Transform Based Lossy Compression of Multispectral Images

Kaarna

Parkkinen

2001

Pattern Analysis & Applications

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We have composed compression methods, which are based on the one-, two-and three-dimensional wavelet transforms. In addition, clustering the spectra of the image is considered. These compression methods, combining compression in the spectral and spatial domains, are compared by using a dataset of 65 multispectral images. The experimental results show that, at low compression ratios, the best method is based on the three-dimensional wavelet transform, where the spatial and spectral dimensions are transformed simultaneously. At higher compression ratios, a high quality wavelet-based compression method in the spatial dimensions combined with the principal component analysis in the spectral dimension gives the best results. This means that components of compressed images can be used as features in pattern analysis applications. The selection of the compression parameters is image-dependent. The results also show that, for multispectral images, both spectral and spatial compression could be used, and for best results, optimal bit allocation in encoding should be found. The results from the methods suggested are compared to the results found in the literature.

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Lossless asymmetric single instruction multiple data codec

Wassenberg

2011

Softw Pract Exp

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This report introduces a new lossless asymmetric single instruction multiple data codec designed for extremely efficient decompression of large satellite images. A throughput in excess of 3GB/s allows decompression to proceed in parallel with asynchronous transfers from fast block devices such as disk arrays. This is made possible by a simple and fast single instruction multiple data entropy coder that removes leading null bits. Our main contribution is a new approach for vectorized prediction and encoding. Unlike previous approaches that treat the entropy coder as a black box, we account for its properties in the design of the predictor. The resulting compressed stream is 1.2 to 1.5 times as large as JPEG-2000, but can be decompressed 100 times as quickly - even faster than copying uncompressed data in memory. Applications include streaming decompression for out of core visualization. To the best of our knowledge, this is the first entirely vectorized algorithm for lossless compression

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Spectral and spatial decorrelation of Landsat-TM data for lossless compression

Cited by 44 publications

References 17 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

Transform Based Lossy Compression of Multispectral Images

Lossless asymmetric single instruction multiple data codec

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