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A ROBUST SPECTRAL TARGET RECOGNITION METHOD FOR HYPERSPECTRAL DATA BASED ON COMBINED SPECTRAL SIGNATURES IGARSS 2011 Vancouver, 24-29 July Xiao Fan, Ye.

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Presentation on theme: "A ROBUST SPECTRAL TARGET RECOGNITION METHOD FOR HYPERSPECTRAL DATA BASED ON COMBINED SPECTRAL SIGNATURES IGARSS 2011 Vancouver, 24-29 July Xiao Fan, Ye."— Presentation transcript:

1 A ROBUST SPECTRAL TARGET RECOGNITION METHOD FOR HYPERSPECTRAL DATA BASED ON COMBINED SPECTRAL SIGNATURES IGARSS 2011 Vancouver, 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

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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

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14 Experiment 1 Area under the ROC curve with Pf from 0 to 1 Category Detector AsphaltMeadowsGravelTrees Metal sheet Bare soilBitumenBricksShadow SAM SID Linear Poly Poly Gaussian

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

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17 Experiment 2 Area under the ROC curve with Pf from 0 to 1 CategoryAsphaltMeadowsGravelTrees Metal sheet Bare soilBitumenBricksShadow variance (10 -3 ) reflective derivative

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

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20 Experiment 3 Area under the ROC curve with Pf from 0 to 1 Category Detector AsphaltMeadowsGravelTrees Metal sheet Bare soilBitumenBricksShadow Worse Equal P= P= Better

21 Thank You


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