Title
Faster and Scalable Parallel Processing Solution to Remove Visual Obstacles from Satellite Imagery
Abstract
The use of artificial satellites, especially? from ESA Copernicus program, created new opportunities for sciences geared toward studying phenomena that have a major impact on our planet, especially anthropogenic phenomena. In this way, accurate measurements and predictions could be made regarding the degree of pollution of land and water, the evolution of deforestation and desertification. However, in order to obtain relevant data from satellite imagery, they must be passed through a procedure to remove visual obstacles, such as clouds, shadows, and sometimes snow. An important drawback of filtering algorithms is the extremely low performance that makes some processing last from a few hours to a few days. This paper attempts to eliminate the major disadvantage of extensive data processing time by proposing a much faster and scalable parallel processing solution. The paper starts from the context setting and the theoretical description of the filtering algorithm used and the main optimization technique, then goes on to detail the actual implementation and ends with the exposition of the results obtained from the extensive qualitative validation and the measurement of the performance indices and efficiency.
Year
DOI
Venue
2019
10.1109/CSCS.2019.00040
2019 22nd International Conference on Control Systems and Computer Science (CSCS)
Keywords
Field
DocType
Satellite imagery,Parallel processing,Scalability,Optimization,Landsat,Cloud detection algorithm
Drawback,Data processing,Satellite,Satellite imagery,Computer science,Parallel processing,Filter (signal processing),Real-time computing,Scalable parallel processing,Scalability
Conference
ISSN
ISBN
Citations 
2379-0474
978-1-7281-2332-5
0
PageRank 
References 
Authors
0.34
2
7
Name
Order
Citations
PageRank
Andra-Teodora Ilie100.34
Ion-Dorinel Filip281.67
Andrei Vlad Postoaca311.38
Catalin Negru44110.94
Florin Pop567490.24
Adrian Stoica667190.24
Florin Serban700.68