Tim Ren, M.S. Candidate Department of Natural Resources Science University of Rhode Island

1. Artificial Neural Network Application in Remote Sensing Tim Ren, M.S. Candidate Department of Natural Resources Science University of Rhode Island 04/28/2000

2. Preview • Introduction to Remote Sensing • Objectives • Why ANN (Artificial Neural Network) • How ANN works • ANN Application in Remote Sensing

3. Part I: Introduction to Remote Sensing

4. Remote Sensing Data Collection • Satellite Multispectral Data of EMR • Analog/Digital Transformation • Multi-band Digital Image (False color image)

5. Remote Sensing of the Earth Surface Improve classification performance: How to achieve an accurate land cover map?

6. Multi-source Spatial Data Band1 Band2 … … Band N GIS Aerial photo ... Multisource spatial data provide information from different perspectives in data modeling and information extraction.

7. Water Forest Agri. Urban Wetland Digital Image Processing Multi-source data visualize A picture Classification result classify

8. Traditional method of classification • Assumption • Methodology • - Unsupervised Classification • - Supervised Classification • Accuracy of Classification • Limits

9. Objectives • Develop artificial neural network algorithms to handle multispectral and multitemporal remote sensing and multisource spatial data • Build efficient ANN architecture, establish learning rules to train and refine ANN paradigms • Apply the trained ANNs in remote sensing data modeling (classification and change detection)

10. Why ANN ? • No need for the Gaussian (Normal) distribution about the input data (as required by Bayesian classifier) • No need for the prior knowledge about the input data before the classification process • No restrictions about the format of input data (More flexible and robust in multi-source spatial data classification; A Promising alternative to Bayesian classification)

11. Classification Process Landsat TM Band1 Band2 Band3 Band4 Band5 Band6 Band7 Observation space Solution space Mapping Relationship 0~255 0~255 0~255 0~255 0~255 0~255 0~255 Category 1 Category .. Category … Category … Category … Category … Category N Water wetland Forest Agri. Urban Residential Methods: Linear Non-linear Statistical ANN 40 45 61 193 80 112 25 (Pattern) Forest

12. Questions to Answer - Does ANN algorithm perform better than traditional statistical method? - Which ANN paradigm is better (Backpropagation? Modularized ANN?...) - How effective an ANN can do in multisource spatial data analysis and modeling?

13. Part II: Introduction to Artificial Neural Networks … … … … … … … … …

14. … … … … … … … … … Artificial Neural Network Is Defined by ... • Processing elements • Organized topological structure • Learning rules

15. Artificial Neural Network Is Defined by ... • Processing elements • Organized topological structure • Learning rules

16. Processing elements (PE) Artificial counterparts of neurons in a brain PE Wj1 Wj2 Wj3 output path  f(x) input Wj4 Wj5

17. ANN Architecture -Processing Elements … … … … … … … … … PE Output PE Input unitj wj1 o1 wj2 o2 oj Σ, f o3 wj3

18. Artificial Neural Network Is Defined by ... • Processing elements • Organized topological structure • Learning rules

19. ANN Architecture -Topological Structure Input layer Hidden layer Output layer … … … … … … … … … Input vector i(x1, x2, … xn) Output vector i(o1, o2, … om)

20. Organized topological structure

21. Organized topological structure --Back-Propagation ANN Architecture Land-cover Categories Output layer Hidden layer Input layer Landsat TM, GIS...

22. Artificial Neural Network Is Defined by ... • Processing elements • Organized topological structure • Learning rules

23. ANN Architecture - Learning Rules Input layer Hidden layer Output layer … … … … … … … … … Input vector i(x1, x1, … xn) Output vector i(o1, o1, … on) How the ANN learns ?

24. Supervised Learning with a Teacher - Paired training set ( Input and Output)

25. Unsupervised Learning - Self-Organize

26. Reinforcement Learning - Learning with Critic

27. Part III ANN application in Remote Sensing … … … … … … … … … ?

28. ANN application in Remote Sensing - Multi-source Spatial Data Classification - Change Detection - Land Cover Change and Prediction

29. Water Forest Agri. Urban Wetland ANN applied in the Remote Sensing - Multi-source Spatial Data Classification Remote Sensed Data Classification Result: land cover map

30. - Multi-source Spatial Data Classification Input layer Hidden layer Output layer … … … … … … … … … … Remote Sensed Data Grassland Woodland … … … … … Wetland … … … … Other source GIS, Airphoto ……. Urban

31. Water Forest Agri. Urban Wetland Forest - Urban Agri. - Urban Urban Unchanged ANN Applied in Remote Sensing - Change Detection Changes between 1985 - 1997 1985 1997

32. - Change Detection (2m:n1:o) network Chang Map with Complete Land Cover Change Information Land cover change extractor Image A Image B

33. ANN Application in Remote Sensing - Land Cover Change and Prediction 1980 1990 2010 ?

34. Plan of Research • - Study Area • - Data • Landsat TM • GIS • Field Observation • (USGS EROS Data Center) • - Design of Artificial Neural Network

35. + Summary Data from Other sources Remote Sensing Data

36. Acknowledgement Dr. Y.Q.Wang Dr. Yong Wang NASA Grant No. NAG58829 Apr. 28 2000