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

Conclusions Experiments & Results Method & System Techniques Motivation Content

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

Conclusions Experiments & Results Method & System Techniques Motivation Content

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

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

Conclusions Experiments & Results Method & System Techniques Starting point Content

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

Conclusions Experiments & Results Method & System Techniques Starting point Content

Experimental Data

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

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

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

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

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

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

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