Application of neural network swarm optimization for paddy-field classification from remote sensing data

2012 ◽  
Vol 16 (4) ◽  
pp. 497-501
Author(s):  
Kazuma Mori ◽  
Takashi Yamaguchi ◽  
Jong Geol Park ◽  
Kenneth J. Mackin
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Paddy Field ◽  
Remote Sensing Data ◽  
Swarm Optimization ◽  
Sensing Data

Automobile gross emitter screening with remote sensing data using objective-oriented neural network

2009 ◽  
Vol 407 (22) ◽  
pp. 5811-5817 ◽  
Author(s):  
Ho-Wen Chen ◽  
Hsi-Hsien Yang ◽  
Yu-Sheng Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data

2020 ◽  
Vol 12 (14) ◽  
pp. 2294
Author(s):  
Hua Su ◽  
Haojie Zhang ◽  
Xupu Geng ◽  
Tian Qin ◽  
Wenfang Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Network Architecture ◽  
Climate Changes ◽  
Heat Content ◽  
Remote Sensing Data ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Coefficient Of Determination ◽  
Sensing Data

Retrieving information concerning the interior of the ocean using satellite remote sensing data has a major impact on studies of ocean dynamic and climate changes; however, the lack of information within the ocean limits such studies about the global ocean. In this paper, an artificial neural network, combined with satellite data and gridded Argo product, is used to estimate the ocean heat content (OHC) anomalies over four different depths down to 2000 m covering the near-global ocean, excluding the polar regions. Our method allows for the temporal hindcast of the OHC to other periods beyond the 2005–2018 training period. By applying an ensemble technique, the hindcasting uncertainty could also be estimated by using different 9-year periods for training and then calculating the standard deviation across six ensemble members. This new OHC product is called the Ocean Projection and Extension neural Network (OPEN) product. The accuracy of the product is accessed using the coefficient of determination (R2) and the relative root-mean-square error (RRMSE). The feature combinations and network architecture are optimized via a series of experiments. Overall, intercomparison with several routinely analyzed OHC products shows that the OPEN OHC has an R2 larger than 0.95 and an RRMSE of <0.20 and presents notably accurate trends and variabilities. The OPEN product can therefore provide a valuable complement for studies of global climate changes.

Neural network classification of remote-sensing data

1995 ◽  
Vol 21 (3) ◽  
pp. 377-386 ◽  
Author(s):  
Diane M. Miller ◽  
Edit J. Kaminsky ◽  
Soraya Rana
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Neural Network Classification
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