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