A New Low Complexity KLT for Lossy Hyperspectral Data Compression

Penna, B.; Tillo, Tammam; Magli, Enrico; Olmo, Gabriella

doi:10.1109/igarss.2006.904

Cited by 44 publications

(28 citation statements)

References 7 publications

(5 reference statements)

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“…the corresponding one based on WT as a spectral transform. This behavior was observed in all our experiments (with an even larger gap for the Landsat TM image, as expected) and is also in agreement with recent findings in the literature [10], [11], [50] where gains of similar entities have been observed when KLT replaces WT. Therefore, we will not consider WT anymore as a possible spectral transform.…”

Section: B Spectral Transformsupporting

confidence: 81%

Region-Based Transform Coding of Multispectral Images

Cagnazzo

Poggi

Verdoliva

2007

IEEE Trans. on Image Process.

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Abstract-We propose a new efficient region-based scheme for the compression of multispectral remote-sensing images. The region-based description of an image comprises a segmentation map, which singles out the relevant regions and provides their main features, followed by the detailed (possibly lossless) description of each region. The map conveys information on the image structure and could even be the only item of interest for the user; moreover, it enables the user to perform a selective download of the regions of interest, or can be used for high-level data mining and retrieval applications. This approach, with the multiple pieces of information required, may seem inherently inefficient. The goal of this research is to show that, by carefully selecting the appropriate segmentation and coding tools, region-based compression of multispectral images can be also effective in a rate-distortion sense, thus providing an image description that is both insightful and efficient. To this end, we define a generic coding scheme, based on Bayesian image segmentation and on transform coding, where several key design choices, however, are left open for optimization, from the type of transform, to the rate allocation procedure, and so on. Then, through an extensive experimental phase on real-world multispectral images, we gain insight on such key choices, and finally single out an efficient and robust coding scheme, with Bayesian segmentation, class-adaptive Karhunen-Loève spectral transform, and shape-adaptive wavelet spatial transform, which outperforms state-of-the-art and carefully tuned conventional techniques, such as JPEG-2000 multicomponent or SPIHT-based coders.

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Section: B Spectral Transformsupporting

confidence: 81%

Region-Based Transform Coding of Multispectral Images

Cagnazzo

Poggi

Verdoliva

2007

IEEE Trans. on Image Process.

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“…A single KLT matrix can be used for all the regions ie., for the whole image. Since the work deals with classes, the spectral dependencies in the multispectral image can be efficiently removed by applying different KLT matrices [4] based on the data type ie., whether smooth or textured region. The technique is termed as class adaptive KLT.…”

Section: Class Adaptive Kltmentioning

confidence: 99%

Adaptive Encoding Algorithm for Multispectral Images

Deepa¹

2010

IJCA

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ABSTRACT-A new adaptive multispectral imagecompression technique based on the regions identified is proposed. The algorithm is adaptive in the sense that according to the data type class of the region, appropriate encoding technique is chosen. The image is first segmented by means of Region splitting and merging procedure based on the statistical characteristic of the image. Then class adaptive hotelling transform or Karhunen Loeve transform (KLT) in the spectral domain and the shape adaptive wavelet transform in the spatial domain are adopted in the image by considering the spatial, spectral and statistical properties which are unique to the multispectral images. The quadtree is used for determining the transform block size and a single KLT matrix is used for the regions of same class ie., class adaptive KLT is applied and the transformation is followed by shape adaptive wavelet transform(SAWT) incorporating the spatial and structural properties of the multispectral image. After transformation, based on the regions identified, if the region is relatively uniform or smooth, the SPIHT (Set Partitioning in Hierarchical Trees) algorithm is adopted. If not, that is, if the region is highly textured in nature, then object based wavelet method is used for compression. Thus the advantages of both SPIHT algorithm and object based wavelet encoding method, both in terms of visual quality and PSNR values, are incorporated in a single compression technique.

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“…PCA, also known as the Karhunen-Loève transform (KLT), has been widely employed for spectral decorrelation in hyperspectral imagery; PCA has exhibited efficient rate-distortion performance in practice for lossy compression, typically outperforming DWT-based spectral decorrelation by a wide margin (e.g., [1,2]). The use of PCA is also further motivated by the fact that it provides theoretically optimal decorrelation in a certain statistical sense [6].…”

Section: Introductionmentioning

confidence: 99%

“…Since hyperspectral data exhibits a high degree of correlation not only spatially but also spectrally, a popular paradigm for the compression of hyperspectral imagery consists of the application of some spatial 2D transform applied in conjunction with a separate 1D spectral transform, followed by a suitable 3D image-compression scheme. Quite often, the spatial transform is a 2D discrete wavelet transform (DWT), while a number of techniques have been employed for the spectral transform, including principal component analysis (PCA) (e.g., [1,2]) and a 1D spectral DWT (e.g., [3]). Part 2 of the JPEG2000 standard [4] supports these and other linear spectral transforms representable as matrix-vector multiplication.…”

Section: Introductionmentioning

confidence: 99%

Spectral-decorrelation strategies for the compression of hyperspectral imagery

Tamhankar

Fowler

2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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Abstract-Several linear transforms with constructions more general than that of principal component analysis are considered for spectral decorrelation in the compression of hyperspectral imagery. Specifically, orthogonal nonnegative matrix factorization, generalized principal component analysis, and principal component analysis coupled with explicit segmentation based on spectral angle mapping are considered. These spectraldecorrelation techniques are employed in conjunction with wavelet-based spatial decorrelation for hyperspectral compression using a 3D version of the well-known SPIHT algorithm. A shape-adaptive wavelet transform and shape-adaptive SPIHT coder are used in the case of the latter two spectral-decorrelation techniques which segment the hyperspectral dataset into multiple distinct pixel classes. Experimental results reveal that, despite their general formulation, the proposed techniques fail to offer spectral-decorrelation performance superior to that of traditional principal component analysis.

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A New Low Complexity KLT for Lossy Hyperspectral Data Compression

Cited by 44 publications

References 7 publications

Region-Based Transform Coding of Multispectral Images

Region-Based Transform Coding of Multispectral Images

Adaptive Encoding Algorithm for Multispectral Images

Spectral-decorrelation strategies for the compression of hyperspectral imagery

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