Geographic Features Extraction Using Soft-computing Methods(2)

1. Geographic Features Extraction Using Soft-computing Methods(2) Chang, Kuan-Tsung MhUST, Dept. of Civil Eng. ktchang@must.edu.tw

2. Outline • Introduction of Artificial Neural Networks(ANNs) • Gain and Loss for the ANNs • Multi-Layered Perceptron (MLP) • Components of a MLP • Forward computation • Back-propogation learning model • Case study on ANNs • Interpretation Key Rules • Scoring scheme for the rules • Data process • Results & Conclusions

3. Introduction of ANNs • 類神經網路是一種計算系統 • 使用大量相連結的人工神經元(Artificial Neuron) 模擬生物神經元(Neuron) 能力 • 人工神經元受到一組量化的輸入訊號刺激並產生回應 • 應用網路權值的作用，傳遞到相連的其他神經元 • 適當地調整網路權值，來記錄所學的知識訊號，以達生物神經網路學習效果

4. BiologicalNeuron Model Axon Cell Dendrite Synapse ArtificialNeuron Model Input Signal X1 Threshold Transfer func. Input Signal X2 Input Signal Xi Output Aj Linked Weight Input Signal Xa

5. Gain and Loss for the ANNs

6. Input Layer Hidden Layers Output Layer Multi-Layer Perceptron (MLP)

7. History for the MLP • Werbos(1974)深感類神經網路感知機模式欠缺隱藏層的缺點，首先提出倒傳遞類神經網路模式 • Rumelhart 等於1986年再次提出 • 採用最陡坡降法的概念，將誤差函數最小化。 • 轉換函數如Sigmoidal func.

8. A Matlab code for the forward computation Hagan et al., 1995

9. Back-propogation Learning Model A

10. Case study on ANNs • Landslide features interpreted by ANN method using a high-resolution satellite image and digital topographic data • Presented on ISPRS XX Congress 2004, held at Istanbul, Turkey

11. Motivation • Landslides are natural phenomena for the dynamic balance of earth surface • Due to the frequent occurrences of Typhoons and earthquake activities in Taiwan, mass movements are common threatens to our lives

12. Properties of interpretation methods • In-situ Surveying • GPS • Labour intensive, point-by-point • Manual photo-interpretation • Stereo visualization • Domain knowledge • A time-consuming and costly approach • Image interpretation • Semi-automatic or full automatic • Efficiency depends on resolution, signatures and methodology

13. Interpretation Key Rules • Colour Tone Criterion • bare lands • Location Criterion • the vicinity of ridge lines, road sides, or the cut-off side of a river • Shape Criterion • spoon-shaped ortree-shaped in river basins • triangular or rectangular-shape if located near river banks • Direction Criterion • the direction of gravity or perpendicular to flow-lines • Shadow Criterion • river bottoms and ridges location in 2D images

14. Scoring scheme for the rules(1)

15. Scoring scheme for the rules(2)

16. Jeou-fen-ell mountain Taiwan Strait Central Mountain Nantou county Test field Test field • Jeou-fen-ell mountain • Site area is 3km x 3 km • At Nantou county of central Taiwan • A typical area of landslides after Chi-Chi earthquake on 1999/9/21

17. 5. Test data and process

18. R E G I S T R A T I O N Satellite image IR and R band NDVI Buffer zone Ridge line Lidar data DEM Slope Vector data River Buffer zone Intersection Buffer zone Road Reference Data process

19. Results for MLP learning error

20. Accuracy for ANN training

21. Discussions • The mismatch of the date of various information sources • Quickbird images were taken on 15 Jan 2003; • The LIDAR point clouds, in May 2002; • Digital vectors, in August 2002; • The manual-interpretated, in 1999. • The cut-off side of a river and shape criteria are not implemented

22. Conclusions • Color tone criterion is better correlated with the target • A successful recognition rate of 85% for landslide and 75% for non-landslide • The GIS and an image analysis system are required for landslide interpretation

23. References • Zurada, J. M., 1992. Introduction to Artificial Neural Systems, West Pub. Co., pp.163-248. • 邵泰彰，1999，類神經網路於多光譜影像分類之應用，交大土木所碩士論文 • 張崑宗，2003，以專家系統及高解析衛星影像進行崩塌地判釋之研究，國科會專題計劃報告 • Chang, K. T. and J. K. Liu, 2004. Landslide features interpreted by neural network method using a high-resolution satellite image and digital topographic data, proceedings of ISPRS XX Congress, Istanbul, Turkey