Multispectral Image Compression By Wavelet / Karhunen-loeve Transformation

Epstein, Benjamin R.; Hingorani, R.; Shapiro, J.M.; Czigler, M.

doi:10.1109/igarss.1992.576801

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Karhunen-Loeve transformation and its additive models of Abbas and Fahmy (1992) and Kouassi et al (2001) is a wellknown dimensionality reduction technique, maybe leads to a description of multidimensional data in which the axis variable are uncorrelated, with the first variable (or component) containing most of the variance of the original data set (Epstein et al 1992;Liu 1999) and the succeeding components containing decreasing proportions of data scatter (Gastpar et al 2006;Siljestrom Ribed and Moreno López 1995;Singh and Harrison 1985). The data decorrelation produced in this process is extremely significant in change detection analysis in multitemporal Landsat multi-spectral image data (Elhag 2016;Li et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Optical Remote Sensing Data in Burned Areas Mapping of Thasos Island, Greece

et al. 2020

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Forest fires are a common feature in the Mediterranean forests through the years, as a wide tract of forest fortune is lost because of the incendiary fires in the forests. The enormous damages caused by forest fires enhanced the efforts of scientists towards the attenuation of the negative effects of forest fire and consequently the minimization of biodiversity losses by searching more for the adequate distribution of attempts on forest fire prevention and, suppression. The multi-temporal Principal Components Analysis is applied to a pair of images of consecutive years obtained from Landsat-8 satellite to unconventional map and assess the spatial extent of the burned areas on the island of Thasos, Greece. First, the PCA was applied on the before fire image, and then a multi-temporal image is created from the 3rd, 4th, and 5th band of before and after images including Normalized Difference Vegetation Index to enhance the results. The results from the different steps of this analysis robustly mapped the burned areas by 82.28 ha confirmed by almost 85%. Are compared with data provided by the local forest service in order to assess their accuracy. The multi-temporal PCA outputs including NDVI (PC 4, PC %, and PC 6) give better accuracy due to its ability to distinguish the burned areas of older years and to the Normalized Difference Vegetation Index that gives better variance to the image.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Optical Remote Sensing Data in Burned Areas Mapping of Thasos Island, Greece

et al. 2020

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“…When multispectral images are coded by transform coding, spectral and spatial transforms are usually independently and sequentially applied. 5 A typical multispectral image compression system is composed of Karhunen-Loeve transform (KLT) as the spectral transform and discrete cosine transform (DCT) or discrete wavelet transform (DWT) as the spatial ones, followed by quantization and encoding.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Image Compression for High Fidelity Colorimetric and Spectral Reproduction

Yu¹,

Murakami²,

Obi³

et al. 2006

jist

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The article aims to provide a solution for multispectral image compression for high color reproducibility with preservation to spectral accuracy. In the method previously proposed to reduce the colorimetric error of the reconstructed multispectral image, a weighting matrix is incorporated to Karhunen-Loeve transform (KLT) as the spectral transform for multispectral image compression, which accounts for the color matching functions of human observers as well as the viewing illuminants. However, the colorimetric improvements are obtained on the cost of degradation of spectral accuracy. In this paper, we show that the reduction of colorimetric error and the preservation of spectral accuracy is a tradeoff that can be controlled by adding a diagonal matrix that is composed of a scalar multiple of an identity matrix to the weighting matrix of KLT. As the result, the small values in the weighting matrix can be lifted up, thus reduce the spectral errors in the corresponding reconstructed multispectral image bands. We implement a multispectral image compression system that integrates the proposed spectral transforms with the addition of diagonal matrix and JPEG2000 for high colorimetric and spectral reproducibility. Experimental results for three 16-band multispectral images show that spectral accuracy can be improved without loss of substantial color reproducibility if the magnitude of the scalar in the diagonal matrix is chosen appropriately

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