Image compression based on wavelet transform for remote sensing

Heung K. Lee; Seong W. Kim; Kyung S. Kim; Soon D. Choi

doi:10.1117/12.197239

Image compression based on wavelet transform for remote sensing

Heung K. Lee, Seong W. Kim, Kyung S. Kim, Soon D. Choi

Department of Computer Science and Engineering

Research output: Contribution to journal › Conference article › peer-review

4 Scopus citations

Abstract

In this paper, we present an image compression algorithm that is capable of significantly reducing the vast amount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet transform to remove the spatial redundancy. The transformed images are then encoded by hilbert-curve scanning and run-length-encoding, followed by huffman coding. We also present the performance of the proposed algorithm with the LAND-SAT MultiSpectral Scanner data. The loss of information is evaluated by PSNR(peak signal to noise ratio) and classification capability.

Original language	English
Pages (from-to)	204-214
Number of pages	11
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	2318
DOIs	https://doi.org/10.1117/12.197239
State	Published - 21 Dec 1994
Event	Recent Advances in Remote Sensing and Hyperspectral Remote Sensing 1994 - Rome, Italy Duration: 26 Sep 1994 → 30 Sep 1994

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title = "Image compression based on wavelet transform for remote sensing",

abstract = "In this paper, we present an image compression algorithm that is capable of significantly reducing the vast amount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet transform to remove the spatial redundancy. The transformed images are then encoded by hilbert-curve scanning and run-length-encoding, followed by huffman coding. We also present the performance of the proposed algorithm with the LAND-SAT MultiSpectral Scanner data. The loss of information is evaluated by PSNR(peak signal to noise ratio) and classification capability.",

author = "Lee, {Heung K.} and Kim, {Seong W.} and Kim, {Kyung S.} and Choi, {Soon D.}",

note = "Publisher Copyright: {\textcopyright} 1994 SPIE. All rights reserved.; Recent Advances in Remote Sensing and Hyperspectral Remote Sensing 1994 ; Conference date: 26-09-1994 Through 30-09-1994",

year = "1994",

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T1 - Image compression based on wavelet transform for remote sensing

AU - Lee, Heung K.

AU - Kim, Seong W.

AU - Kim, Kyung S.

AU - Choi, Soon D.

PY - 1994/12/21

Y1 - 1994/12/21

N2 - In this paper, we present an image compression algorithm that is capable of significantly reducing the vast amount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet transform to remove the spatial redundancy. The transformed images are then encoded by hilbert-curve scanning and run-length-encoding, followed by huffman coding. We also present the performance of the proposed algorithm with the LAND-SAT MultiSpectral Scanner data. The loss of information is evaluated by PSNR(peak signal to noise ratio) and classification capability.

AB - In this paper, we present an image compression algorithm that is capable of significantly reducing the vast amount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet transform to remove the spatial redundancy. The transformed images are then encoded by hilbert-curve scanning and run-length-encoding, followed by huffman coding. We also present the performance of the proposed algorithm with the LAND-SAT MultiSpectral Scanner data. The loss of information is evaluated by PSNR(peak signal to noise ratio) and classification capability.

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DO - 10.1117/12.197239

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SN - 0277-786X

VL - 2318

SP - 204

EP - 214

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Recent Advances in Remote Sensing and Hyperspectral Remote Sensing 1994

Y2 - 26 September 1994 through 30 September 1994

ER -

Image compression based on wavelet transform for remote sensing

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