1. Window based software for Neuro-Fuzzy Classification of Remotely Sensed Image (Stand along application and extension for ArcGIS) Xiaogang Yang POEC 6387 GIS Workshop Final presentation Director: Dr. Fang Qiu Dr. Ron Briggs

2. Contents Background and Objectives Methodology Challenge Issue Progress and Results Case Study Conclusion Future Work

3. Background and Objectives 1.Project background  Based on the research work of Dr. Fang Qiu: Neuro-Fuzzy Classification of Remotely Sensed Image “Neuro-Fuzzy Classification of Remotely Sensed Image”  Unix and C/C++ language environment.  Erdas Image dependent 2. Objective:  Develop window based software for Neuro-Fuzzy Classification of Remotely Sensed Image  Design Graphic Use Interface.  Erdas Image independent  Design a Extension for ArcGIS.

4. Methodology First Part: (before mid presentation) Stand along window based Software Coding: Microsoft VC++, recoding from C ->VC++ GUI: MFC Dialogue based Interface. VC6 and VC.NET compatible

5. Methodology Cont. Second Part: Extension for ArcGIS (ArcMap) VC/VB/ArcObject. Active DLL project: easy to be used by ArcMap. Provide factions: Sampling, Training, Classification, etc.

6. Challenge Issue 1.Programming skill. –Language: C, C++, VB, ArcObject, –Tools: VC6, MFC, VB6, VBA, DLL, Active Control. 2.Large remote sensed image file: –20MB-200MB. 3.Performance: –Memory issue: –Running speed: C++ for image processing (Training and Classification) VB for Sampling BIP format

7. Stand along application Easy to use without Any tools Provided the Training Data. Click to start the Demo:

8. Stand along application Enter Page Screen Shot

9. Stand along application Cont. Main page Screen Shot

10. Stand along application Cont. Training Screen Shot

11. Weight File Standard Deviation File Stand along application Cont. Stand along application Cont. Training Results one

12. Stand along application Cont. Stand along application Cont. Training Results Two

13. Stand along application Cont. Stand along application Cont. Aeverage Error per Pattern vs. Training Cycle

14. Classification Screen Shot Stand along application Cont.

15. Case Study one Study Area: Jacksonville Beach, FL Data Set: National Aerial Photography Program (NAPP) Digital Orthophotoquad, 1 x 1 m, RGB = NIR, R, G, Size: 19.162 KB

16. GIS Extension Click to demo GIS Customization. Function: Sampling, Training, Classification. Etc.

17. GIS Extension Cont. GIS Extension Cont. (Sampling)

18. GIS Extension Cont. GIS Extension Cont. (Training)

19. GIS Extension Cont. GIS Extension Cont. (Classification)

20. Case Study two Remote Sensed Image for North Texas Data prepare Data Source: TM7 Path 027 Row 037 8/19/2000 Original Geo TIFF Format 9 file. Use Erdas Image: –combined 6 bands TIFF file into one IMG file format –Select sub area –convert IMG to BIP format, Size: 44MB

21. Case Study two Map for North Dallas

22. Case Study two Remote Sensed Image for North Dallas (Top:2186805.75, Left:702668.75, Bottom: 2125958.25, Right797773.25) Band1 Band3 Band5

23. Case Study two Remote Sensed Image for North Dallas Classification Water Bare & Grassland Forest Urban

24. Case Study two map (Near City of Rowlett)

25. Case Study two Case Study two Aerial Photo (Near City of Rowlett)

26. Case Study two Remote sensed image (Near City of Rowlett)

27. Case Study two Case Study two Image Classification (Near City of Rowlett)

28. Conclusion The neruo fuzzy network is a very power method for remote sensed image classification This Software provide a tool for image classification. Challenge: How to select sampling pixels? experience?

29. Future Work Improvement Add more functions to this extension –Analysis? Statistic? Graphic? –Conversion? Create shape point file during sampling. –Easy to visualize the location classification and distribution of sampling points. –Modify the points (location and class) as needed. –Using shape file instead of native txt file during training.

30. End

31. Neuro-fuzzy Learning, Classification, Deffuzification Learning Rules: Learning Rules:  w ij = r (x-w ij ) (i is winner)  w ij = -r (x-w ij ) (Otherwise)  ij = r (|w ij -x|-  ij  (i is winner)  ij = 0 (Otherwise) Fuzzy Classification: Fuzzy Classification: F i (I) = (exp((-1/2)  j (w ij -I j ) 2 /  ij 2 )) 1/n Defuzzification: Defuzzification: F w (I) = max (F i (I): i = 1…m)