This presentation summarizes artificial neural networks (ANN) and their application in remote sensing image classification. ANN aims to mimic the human brain's structure and learning. The backpropagation ANN learns from labeled training data by adjusting weights to minimize error through forward and backward propagation. ANN can classify remote sensing imagery by learning pixel band patterns. A case study classified wetland types from satellite imagery using ANN, achieving 71-79% accuracy. While ANN shows potential for remote sensing classification, accuracy can be improved through preprocessing and model enhancements.

1. Algorithm in GeoInformatic
Artificial Neural Network.
Overview and Application In Remote Sensing
Presented By
Decky Aspandi Latif
56070701073
Tuesday, November 12, 2013
Computer Engineering
King Mongkut University of Technology
2013

2. Introduction
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Remote Sensed Area
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Comprised of the Great amount of data
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Classification useful for :
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Management, Monitoring, Administration,etc
Other Classification Technique
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Manual Digitization
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Supervised & unsupervised.
Tuesday, November 12, 2013

3. Basic of ANN
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Researcher's Attempt to model the Human
brain.
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Mimic the Structure and Learning process
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Supervised and Unsupervised Learning
Tuesday, November 12, 2013

4. Backpropagation ANN in Brief
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Learning from the data set (supervised)
Using weight and activation function →
Error : 0.xx Feed forward → generating output
output
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1
1
0
1
i1
i2
0.3
0.5
-0.2
-0.8
0.7
0.2
0.8
1.1
-1.4
-1.3
Tuesday, November 12, 2013
0.2
-0.9
-0.8
0.4
0.3
-0.7
0.8
0.7
5.7
1.3
1.2
-0.2
-0.3
0.2
E Propagate
Feed Forward
Feed Forward
1.2
o1
Propagate the error → update
weight → learning !
Real Data
Training Data
No. i1 i2 o1 o2
1
0.1
o2
1
1
1
0
?
1
?
2
0
1
0
0
1
?
0
1
?
3
0
1
1
?
0
?
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.
.
?
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?
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?
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1000
1
0
0
1
0
?
1
0
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Prediction
Iteration

5. RS Classification ↔ ANN
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ANN take account on Learning the pattern
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Experience → classify automatically.
No.
Tuesday, November 12, 2013
Water
2
Road
.
7
output
1
.
Input
Type
Grass

6. Application (Basic Idea)
Normalize (0-255) → (0 ↔ 1)
No.
B2
B4
o1
o2
o3
o4
No.
Type
o1
o2
o3
o4
1
0.7
0.2
0
0
0
1
1
Water
0
0
0
1
2
0.1
0.3
0
0
1
0
2
Road
0
0
1
0
3
0.6
0.2
0
0
0
1
.
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7
Grass
0
1
1
1
1000
0.5
0.4
0
1
1
1
Tuesday, November 12, 2013

7. Previous Research
Remote sensing image classification based on artificial neural network: A case study of Honghe
Wetlands National Nature Reserve, wang et all, 2010
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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
Tuesday, November 12, 2013

8. Application Cont..
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The Classification Result
Tuesday, November 12, 2013

9. Application Cont..
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The improvement of the classification is about 8% 71% - 79%
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With > 70% accuracy, potential to be used in some cases.
Tuesday, November 12, 2013

10. Conclusion
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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 12, 2013

11. The End.
Thank You.
Tuesday, November 12, 2013
Computer Engineering
King Mongkut University of Technology
2013