An Architecture for the Compression of Hyperspectral Imagery

Pickering, Mark; Ryan, Michael J.

doi:10.1007/0-387-28600-4_1

Cited by 26 publications

(11 citation statements)

References 42 publications

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“…Although distortion measures shown above can give a general concept of how the reconstructed is distorted, they have little or even no correlation to the degradation in accuracy of subsequent image analysis (Pickering and Ryan 2006). Instead of looking at the rate-distortion performance, quality-assured assessment, such as HSI classification , can be adopted.…”

Section: Evaluation Criteriamentioning

confidence: 99%

“…According to different entropy coding methods, compression algorithms based on DWT can be divided into two groups, which are zero-tree coding and contextbased coding (Pickering and Ryan 2006). The most famous approach for zero-tree coding is the set partitioning in hierarchical trees (SPIHT) (Said and Pearlman 1996), some extensions include 3D SPIHT (Kim, Xiong, and Pearlman 2000) and 3D set partitioning embedded block (SPECK) (Pearlman et al 2004).…”

Section: Introductionmentioning

confidence: 99%

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Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Qiao

Ren

Sun

et al. 2014

International Journal of Remote Sensing

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This version is available at https://strathprints.strath.ac.uk/50084/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Effective compression of hyperspectral imagery using an improved 3DDCT approach for land cover analysis in remote sensing applications Effective compression of hyperspectral imagery using an improved 3D DCT approach for land cover analysis in remote sensing applicationsAlthough hyperspectral imagery (HSI), which has been applied in a wide range of applications, suffers from very large volumes of data, its uncompressed representation is still preferred to avoid compression loss for accurate data analysis. In this paper, we focus on quality-assured lossy compression of HSI,where the accuracy of analysis from decoded data is taken as a key criterion to

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Section: Evaluation Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Qiao

Ren

Sun

et al. 2014

International Journal of Remote Sensing

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“…This type of sensors can generate more than one terabyte data in one day. Because of these enormous data volumes, the use of a robust data compression technique has become very important for archiving and transferring purposes [1]. Due to the importance of generating highly accurate information about the atmosphere, clouds, and surface parameters provided by the hyperspectral sensors, lossy compression techniques are not acceptable in this case [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Compression Technique for Dealing with Corrupt Bands and High Levels of Band Correlations in Hyperspectral Images Based on Binary Hybrid GA-PSO for Big Data Compression

Nahavandy¹,

Ghamisi²,

Kumar³

et al. 2015

IJCA

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Hyperspectral sensors generate useful information about climate and the earth's surface in numerous contiguous narrow spectral bands, being widely used in resource management, agriculture, environmental monitoring, among others. The compression of hyperspectral data helps in long-term storage and transmission systems. This paper introduces a new adaptive compression method for hyperspectral data. The method is based on separating the bands with different specifications by the histogram analysis and Binary Hybrid Genetic Algorithm-Particle Swarm Optimization (BHGAPSO). The new proposed method improves the compression ratio of the bestknown JPEG standards, saves storage space, and speeds up the transmission system. The proposed method is applied on two different test cases, and the results are evaluated and compared with a few powerful compression techniques, such as lossless JPEG and JPEG2000. The results confirm that the proposed method is accurate, simple and fast, which can be useful for big data (i.e, a high volume of data) processing. General TermsAlgorithms.

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“…Some types of sensors can generate more than 1 TB of data in one day. Hence the use of a robust data compression techniques has become very important for archiving and transferring purposes 1,2 . Because of the importance of generating highly accurate information about the atmosphere, clouds, and surface parameters provided by the RS sensors, lossy compression techniques are not desirable 3 .…”

Section: Introductionmentioning

confidence: 99%

A new method for compression of remote sensing images based on an enhanced differential pulse code modulation transformation

Ghamisi¹,

Sepehrband²,

Kumar³

et al. 2013

ScienceAsia

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Remote sensing sensors generate useful information about climate and the Earth's surface, and are widely used in resource management, agriculture, and environmental monitoring. Compression of the RS data helps in long-term storage and transmission systems. Lossless compression is preferred for high-detail data, such as from remote sensing. In this paper, a less complex and efficient lossless compression method for images is introduced. It is based on improving the energy compaction ability of prediction models. The proposed method is applied to image processing, RS grey scale images, LiDAR rasterized data, and hyperspectral images. All the results are evaluated and compared with different lossless JPEG and a lossless version of JPEG2000, thus confirming that the proposed lossless compression method leads to a high speed transmission system because of a good compression ratio and simplicity.

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An Architecture for the Compression of Hyperspectral Imagery

Cited by 26 publications

References 42 publications

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

A Novel Adaptive Compression Technique for Dealing with Corrupt Bands and High Levels of Band Correlations in Hyperspectral Images Based on Binary Hybrid GA-PSO for Big Data Compression

A new method for compression of remote sensing images based on an enhanced differential pulse code modulation transformation

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