Feature Evolution for Classification of Remotely Sensed Data

2007 ◽  
Vol 4 (3) ◽  
pp. 354-358 ◽  
Author(s):  
Demetris Stathakis ◽  
Kostas Perakis
Keyword(s):  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Feature Evolution

scholarly journals Active Learning: Any Value for Classification of Remotely Sensed Data?

2013 ◽  
Vol 101 (3) ◽  
pp. 593-608 ◽  
Author(s):  
Melba M. Crawford ◽  
Devis Tuia ◽  
Hsiuhan Lexie Yang
Keyword(s):  
Active Learning ◽  
Remotely Sensed ◽  
Remotely Sensed Data

scholarly journals Use of Spot Imagery for Estimating Surface Runoff in Sudan

2008 ◽  
Vol 3 (1) ◽  
pp. 10
Author(s):  
Abdullah S. Alsalman
Keyword(s):  
Standard Deviation ◽  
Surface Runoff ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Runoff Volume ◽  
Spot Imagery

Digital classification of remotely sensed data can be very effective in determining volume of runoff. Mean discrepancy value of ± 6 mm between observed and estimated runoff volume was obtained in this experiment with a standard deviation of ±7.2 mm.

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