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

Ghamisi, Pedram, Sepehrband, Farshid, Kumar, Lalit, Couceiro, Micael S. and Martins, Fernando M.L. (2013) A new method for compression of remote sensing images based on an enhanced differential pulse code modulation transformation. ScienceAsia, 39 5: 546-555. doi:10.2306/scienceasia1513-1874.2013.39.546


Author Ghamisi, Pedram
Sepehrband, Farshid
Kumar, Lalit
Couceiro, Micael S.
Martins, Fernando M.L.
Title A new method for compression of remote sensing images based on an enhanced differential pulse code modulation transformation
Journal name ScienceAsia   Check publisher's open access policy
ISSN 1513-1874
Publication date 2013-10
Year available 2013
Sub-type Article (original research)
DOI 10.2306/scienceasia1513-1874.2013.39.546
Open Access Status DOI
Volume 39
Issue 5
Start page 546
End page 555
Total pages 10
Place of publication Bangkok, Thailand
Publisher Science Society of Thailand under the Patronage of His Majesty the King
Collection year 2014
Language eng
Abstract 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.
Keyword Enhanced DPCM transform
Hyperspectral images
LiDAR technology
Lossless compression
Remote sensing
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
Centre for Advanced Imaging Publications
 
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