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Presentation of ANN into Remote Sensing ApplicationTRANSCRIPT
Tuesday, November 5, 2013
Algorithm in GeoInformatic
Artificial Neural Network.Overview and Application In Remote Sensing
Presented By
Decky Aspandi Latif56070701073
Computer EngineeringKing Mongkut University of Technology
2013
Tuesday, November 5, 2013
Introduction
● Remote Sensed Area – Comprised of the Great amount of data
– Classification useful for : ● Management, Monitoring, Administration,etc
● Other Classification Technique– Manual Digitation
– Supervised & unsupervised.
Tuesday, November 5, 2013
Basic of ANN
● Researcher's Attempt to model the Human brain.
● Mimic the Structure and Learning process● Supervised and Unsupervised Learning
Tuesday, November 5, 2013
Backpropagation ANN in Brief
● Learning from the data set (supervised)● Using weight and activation function →
output ● Feed forward → generating output● Propagate the error → update
weight → learning !
i1
i2
0.2
-0.7
1.2
0.1
0.8
0.5
0.7
-1.3
0.8
-0.8
5.7
0.2
1.2
Feed Forward
-0.8 0.3
1.1 -0.3
1
1
i1
i2
0.2
-0.7
1.2
0.1
0.8
0.3
0.7
-1.4
0.2
-0.9
0.7
0.2
1.3
E Propagate
-0.2 0.4
1.1 -0.2
1
1
Error : 0.xx
No. i1 i2 o1 o2
1 1 1 ? ?
2 0 0 ? ?
3 0 1 ? ?
. . . ? ?
. . . ? ?
1000 1 0 ? ?
Prediction
Real Data
No. i1 i2 o1 o2
1 1 1 0 1
2 1 0 1 1
3 0 1 1 0
. . . . .
. . . . .
1000 0 0 0 0
Iteration
Training Data
No. i1 i2 o1 o2
1 1 1 0 1
2 1 0 1 1
3 0 1 1 0
. . . . .
. . . . .
1000 0 0 0 0
Iteration
Training Datai1
i2
0.2
-0.7
o1
o2
0.8
0.3
0.7
-1.4
0.2
-0.9
0.7
0.2
1.3
Feed Forward
-0.2 0.4
1.1 -0.2
1
0
No. i1 i2 o1 o2
1 1 1 1 1
2 0 0 0 0
3 0 1 1 0
. . . . .
. . . . .
1000 1 0 1 1
Prediction
Real Data
Tuesday, November 5, 2013
RS Classification ↔ ANN
● ANN take account on Learning the pattern● Experience → classify automatically.
Input
No. Type
1 Water
2 Road
. .
7 Grass
output
Tuesday, November 5, 2013
Application (Basic Idea)
No. B2 B4 o1 o2 o3 o4
1 0.7 0.2 0 0 0 1
2 0.1 0.3 0 0 1 0
3 0.6 0.2 0 0 0 1
. . . . . . .1000 0.5 0.4 0 1 1 1
No. Type o1 o2 o3 o4
1 Water 0 0 0 1
2 Road 0 0 1 0
. . . . . .
7 Grass 0 1 1 1
Normalize (0-255) → (0 ↔ 1)
Tuesday, November 5, 2013
Previous Research
Remote sensing image classification based on artificial neural network: A case study of Honghe Wetlands National Nature Reserve, wang et all, 2010
● Classification employed to monitor the Wetland → environment
● 6 of 8 Bands of Thematic Mapper (TM) used as input paired with 7 output classes
● Purification is entangled to remove error in imagery → boost classification accuracy.
● Comparison is employed
to see the effectiveness
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Application Cont..
● The Classification Result
Tuesday, November 5, 2013
Application Cont..
● The improvement of the classification is about 8% 71% - 79%
● With > 70% accuracy, potential to be used in some cases.
Tuesday, November 5, 2013
Conclusion
● ANN is Try to model how brain works.● Learning is done through the updated
weight along the iteration.● ANN is applicable to RSS through imagery
classification by learning the pattern of pixel band value.
● Potential of ANN is acceptable, and can greatly increased by some enhancement
Tuesday, November 5, 2013
The End.
Thank You.
Computer EngineeringKing Mongkut University of Technology
2013