Distributed data structure templates for data-intensive remote sensing applications

2012 ◽  
Vol 25 (12) ◽  
pp. 1784-1797 ◽  
Author(s):  
Yan Ma ◽  
Lizhe Wang ◽  
Dingsheng Liu ◽  
Tao Yuan ◽  
Peng Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Structure ◽  
Distributed Data ◽  
Data Intensive ◽  
Sensing Applications ◽  
Remote Sensing Applications

scholarly journals UAV & satellite synergies for optical remote sensing applications: A literature review

2021 ◽  
Vol 3 ◽  
pp. 100019
Author(s):  
Alvarez-Vanhard Emilien ◽  
Corpetti Thomas ◽  
Houet Thomas
Keyword(s):  
Remote Sensing ◽  
Literature Review ◽  
Optical Remote Sensing ◽  
Sensing Applications ◽  
Remote Sensing Applications

RADARSAT-2; are its technical capabilities expected to provide potential for remote sensing applications ?

2003 ◽  
Author(s):  
J.J. van der Sanden ◽  
P. Budkewitsch ◽  
D.G. Flett ◽  
A.L. Gray ◽  
R.K. Hawkins ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sensing Applications ◽  
Remote Sensing Applications

Coral reef remote sensing applications

1986 ◽  
Vol 1 (4) ◽  
pp. 3-15 ◽  
Author(s):  
Deborah A. Kuchler ◽  
David L.B. Jupp ◽  
Daniel B. van R. Claasen ◽  
William Bour
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Sensing Applications ◽  
Remote Sensing Applications

Lossless Compression for Text and Images

1997 ◽  
Vol 08 (01) ◽  
pp. 179-231 ◽  
Author(s):  
Alistair Moffat ◽  
Timothy C. Bell ◽  
Ian H. Witten
Keyword(s):  
Remote Sensing ◽  
Lossless Compression ◽  
Lossy Compression ◽  
International Standards ◽  
Experimental Results ◽  
Extensive Discussion ◽  
Standard Methods ◽  
Sensing Applications ◽  
Other Information ◽  
Remote Sensing Applications

Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as images—particularly bilevel ones, or ones arising in medical and remote-sensing applications, or ones that may be required to be certified true for legal reasons. Moreover, during the process of lossy compression, many occasions for lossless compression of coefficients or other information arise. This paper surveys techniques for lossless compression. The process of compression can be broken down into modeling and coding. We provide an extensive discussion of coding techniques, and then introduce methods of modeling that are appropriate for text and images. Standard methods used in popular utilities (in the case of text) and international standards (in the case of images) are described.

Spectral information analysis of image fusion data for remote sensing applications

2013 ◽  
Vol 28 (4) ◽  
pp. 291-310 ◽  
Author(s):  
Yuhendra Yusuf ◽  
Josaphat Tetuko Sri Sumantyo ◽  
Hiroaki Kuze
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Spectral Information ◽  
Information Analysis ◽  
Sensing Applications ◽  
Fusion Data ◽  
Remote Sensing Applications
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