scholarly journals COMPARISON OF OPEN SOURCE COMPRESSION ALGORITHMS ON VHR REMOTE SENSING IMAGES FOR EFFICIENT STORAGE HIERARCHY

2016 ◽  
Vol XLI-B4 ◽  
pp. 3-9
Author(s):  
A. Akoguz ◽  
S. Bozkurt ◽  
A. A. Gozutok ◽  
G. Alp ◽  
E. G. Turan ◽  
...  
Keyword(s):  
Open Source ◽  
Fundamental Problem ◽  
Post Processing ◽  
Remote Sensing Images ◽  
Telemetry Data ◽  
Compression Algorithms ◽  
Efficient Storage ◽  
Resolution Level ◽  
Lossless Data Compression ◽  
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 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 

scholarly journals COMPARISON OF OPEN SOURCE COMPRESSION ALGORITHMS ON VHR REMOTE SENSING IMAGES FOR EFFICIENT STORAGE HIERARCHY

2016 ◽  
Vol XLI-B4 ◽  
pp. 3-9
Author(s):  
A. Akoguz ◽  
S. Bozkurt ◽  
A. A. Gozutok ◽  
G. Alp ◽  
E. G. Turan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Open Source ◽  
Fundamental Problem ◽  
Image Data ◽  
Satellite Communications ◽  
Compression Algorithms ◽  
Resolution Level ◽  
Lossless Data Compression ◽  
Storage Hierarchy ◽  
Burrows Wheeler Transform

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.

Remote Sensing Image Mosaic Algorithm

2012 ◽  
Vol 500 ◽  
pp. 716-721
Author(s):  
Yi Ding Wang ◽  
Shuai Qin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Hot Spot ◽  
Remote Sensing Image ◽  
Processing Algorithm ◽  
Geometric Features ◽  
Image Mosaic ◽  
Post Processing ◽  
Remote Sensing Images ◽  
Image Pixels

In the field of remote sensing, the acquirement of higher resolution of remote sensing images has become a hot spot issue with widely use of high resolution of remote sensing images. This paper focus on the characteristics of high resolution remote sensing images, on the basis of fully considerate of the correlation between geometric features and image pixels, bring forward a fusion of image mosaic processing algorithm. With this algorithm, the surface features can be well preserved after the processing of mosaic the remote sensing images, and the overlapping area can transit naturally, it will be better for the post-processing, analysis and application.

PITR: A New Open Source R Package for PIT Telemetry Data

Fisheries ◽  
2018 ◽  
Vol 43 (1) ◽  
pp. 5-5 ◽  
Author(s):  
Joel M. S. Harding ◽  
Douglas C. Braun ◽  
Nicholas J. Burnett ◽  
Annika Putt
Keyword(s):  
Open Source ◽  
R Package ◽  
Telemetry Data ◽  
Pit Telemetry

Open source software in a practical approach for post processing of radiologic images

2014 ◽  
Vol 120 (3) ◽  
pp. 309-323 ◽  
Author(s):  
Gianluca Valeri ◽  
Francesco Antonino Mazza ◽  
Stefania Maggi ◽  
Daniele Aramini ◽  
Luigi La Riccia ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Practical Approach ◽  
Post Processing

scholarly journals FPGA implementation of Lempel-Ziv data compression

2021 ◽  
Vol 10 (2) ◽  
pp. 99
Author(s):  
Gody Mostafa ◽  
Abdelhalim Zekry ◽  
Hatem Zakaria
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Digital Communication ◽  
Compression Rate ◽  
High Compression Ratio ◽  
Compression Algorithms ◽  
High Compression ◽  
Space Saving ◽  
Fpga Chip ◽  
Lossless Data Compression

When transmitting the data in digital communication, it is well desired that the transmitting data bits should be as minimal as possible, so many techniques are used to compress the data. In this paper, a Lempel-Ziv algorithm for data compression was implemented through VHDL coding. One of the most lossless data compression algorithms commonly used is Lempel-Ziv. The work in this paper is devoted to improve the compression rate, space-saving, and utilization of the Lempel-Ziv algorithm using a systolic array approach. The developed design is validated with VHDL simulations using Xilinx ISE 14.5 and synthesized on Virtex-6 FPGA chip. The results show that our design is efficient in providing high compression rates and space-saving percentage as well as improved utilization. The Throughput is increased by 50% and the design area is decreased by more than 23% with a high compression ratio compared to comparable previous designs.

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