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. Conclusions Experiments & Results Method & System Techniques Motivation Content

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. Conclusions Experiments & Results Method & System Techniques Motivation Content

5. Techniques 1.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 2.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. Conclusions Experiments & Results Method & System Techniques Starting point Content

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

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 Category Detector AsphaltMeadowsGravelTrees Metal sheet Bare soilBitumenBricksShadow SAM0.8960.89580.9350.95980.99870.80950.970.94370.9441 SID0.89990.85850.9330.95590.99870.80420.96620.94320.9231 Linear0.89780.88940.93450.9590.99870.79870.970.94340.9558 Poly20.95050.89720.93220.95720.99960.77440.98520.96680.9983 Poly30.95050.89680.93220.95380.99960.76610.98520.96680.9983 Gaussian0.95290.93090.94030.9450.99940.83580.98690.97340.9987

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 CategoryAsphaltMeadowsGravelTrees Metal sheet Bare soilBitumenBricksShadow variance (10 -3 ) 13.0574.57783.54562.84921.55951.25050.68270.39480.2574 reflective0.99940.83580.9450.93090.95290.94030.97340.99870.9869 derivative0.99990.83670.97170.86110.92050.90260.89360.96780.9155

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 Category Detector AsphaltMeadowsGravelTrees Metal sheet Bare soilBitumenBricksShadow Worse0.99940.83580.9450.86110.90260.92050.89360.96780.9155 Equal0.99980.84720.95720.92420.94390.95740.9730.99760.9868 P=10.99990.85250.95760.92510.94420.95660.97340.9980.9871 P=40.99990.85660.95910.92730.94370.95370.97380.99860.9872 Better0.99990.83670.97170.93090.94030.95290.97340.99870.9869

21. Thank You Email: fancy_2626@163.com