Open AccessCOMPARISON OF OPEN SOURCE COMPRESSION ALGORITHMS ON VHR REMOTE SENSING IMAGES FOR EFFICIENT STORAGE HIERARCHY
Author(s) -
Alper Akoguz,
S. Bozkurt,
Arif Armagan Gozutok,
G. Alp,
E. G. Turan,
M. Bogaz,
Sedef Kent
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b4-3-2016
Subject(s) - lossless compression , computer science , data compression , image compression , compression (physics) , remote sensing , raw data , compression ratio , sync , file format , algorithm , database , image (mathematics) , computer vision , image processing , geography , telecommunications , engineering , materials science , composite material , internal combustion engine , automotive engineering , programming language , channel (broadcasting)
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