Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 3-9, 2016
https://doi.org/10.5194/isprs-archives-XLI-B4-3-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
10 Jun 2016
COMPARISON OF OPEN SOURCE COMPRESSION ALGORITHMS ON VHR REMOTE SENSING IMAGES FOR EFFICIENT STORAGE HIERARCHY
A. Akoguz1, S. Bozkurt1, A. A. Gozutok1, G. Alp1, E. G. Turan2, M. Bogaz1, and S. Kent3 1Center for Satellite Communications and Remote Sensing, ITU, Istanbul, Turkey
2Department of Geophysical Engineering, ITU, Istanbul, Turkey
3Department of Electronics and Communication Engineering, ITU, Istanbul, Turkey
Keywords: Lossless Data Compression, LZMA, LZO, BWT, PPMd, GeoTIFF, VHR, SPOT, open source Abstract. 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.
Conference paper (PDF, 1167 KB)


Citation: Akoguz, A., Bozkurt, S., Gozutok, A. A., Alp, G., Turan, E. G., Bogaz, M., and Kent, S.: COMPARISON OF OPEN SOURCE COMPRESSION ALGORITHMS ON VHR REMOTE SENSING IMAGES FOR EFFICIENT STORAGE HIERARCHY, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 3-9, https://doi.org/10.5194/isprs-archives-XLI-B4-3-2016, 2016.

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