COMPARISON OF OPEN SOURCE COMPRESSION ALGORITHMS ON VHR REMOTE SENSING IMAGES FOR EFFICIENT STORAGE HIERARCHY
High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B4/3/2016/isprs-archives-XLI-B4-3-2016.pdf |
| Summary: | High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored
after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file
sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to
another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of
processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in
this study aim to provide compression without any loss of data holding spectral information. Within this objective, well-known open
source programs supporting related compression algorithms have been implemented on processed GeoTIFF images of Airbus Defence
& Spaces SPOT 6 & 7 satellites having 1.5 m. of GSD, which were acquired and stored by ITU Center for Satellite Communications
and Remote Sensing (ITU CSCRS), with the algorithms Lempel-Ziv-Welch (LZW), Lempel-Ziv-Markov chain Algorithm (LZMA &
LZMA2), Lempel-Ziv-Oberhumer (LZO), Deflate & Deflate 64, Prediction by Partial Matching (PPMd or PPM2), Burrows-Wheeler
Transform (BWT) in order to observe compression performances of these algorithms over sample datasets in terms of how much of the
image data can be compressed by ensuring lossless compression. |
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| ISSN: | 1682-1750 2194-9034 |