Image compression based on wavelet transform for remote sensing

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

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


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 languageEnglish
Pages (from-to)204-214
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 21 Dec 1994
EventRecent Advances in Remote Sensing and Hyperspectral Remote Sensing 1994 - Rome, Italy
Duration: 26 Sep 199430 Sep 1994


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