IGARSS 2011 Vancouver, 24-29 July

1. A ROBUST SPECTRAL TARGET RECOGNITION METHOD FOR HYPERSPECTRAL DATA BASED ON COMBINED SPECTRAL SIGNATURES IGARSS 2011 Vancouver, 24-29 July Xiao Fan, Ye Zhang, Feng Li, Yushi Chen, Tao Shao, Shuang Zhou from Harbin Institute of Technology, China

2. Content Motivation Techniques Method & System Experiments & Results Conclusions

3. Motivation • Spectral Target recognition • Importance • important application for Hyperspectral Image Processing • Goal • high accuracy & robustness • Problem • spectral variation by complicated imaging environment • Solution

4. Content Motivation Techniques Method & System Experiments & Results Conclusions

5. Techniques • Support Vector Data Description (SVDD) • Inspired by Support Vector Machine (SVM) • A learning machine, first used for anomaly detection in hyperspectral image processing • A detector for spectral target recognition • Alleviate the heterogeneous spectra within homogeneous object

6. Techniques • Spectral signatures • Reflective spectra, most common signatures • Relevant to physical and chemical properties • Illumination variation and terrain undulation • Spectral-amplitude fluctuation • Derivative spectra • Insensitivity to spectral amplitude; sensitivity to spectral slope

7. Content Starting point Techniques Method & System Experiments & Results Conclusions

8. Method & System • Combined spectral signatures • Simply connecting, curse of dimensionality • Combining on gray decision level • Combined weights of the signatures • Based on the role of each signatures • According to the data characteristic

10. Content Starting point Techniques Method & System Experiments & Results Conclusions

11. Experimental Data

12. Experiment 1 Unavoidable noise makes heterogeneous spectra within the homogeneous object SVDD detector vs spectral match-based detector SAM SID SVDD with linear, quadratic polynomial, and cubic polynomial kernel

14. Experiment 1 • Area under the ROC curve with Pf from 0 to 1

15. Experiment 2 • Illumination variation and terrain undulation make the spectral-amplitude fluctuation • derivative spectra vs reflective spectral • mean spectral variance

17. Experiment 2 • Area under the ROC curve with Pf from 0 to 1

18. Experiment 3 • Combined the two spectral signatures by different weights • Equal weights • Unequal weights

20. Experiment 3 • Area under the ROC curve with Pf from 0 to 1

21. Thank You Email: fancy_2626@163.com