A ROBUST SPECTRAL TARGET RECOGNITION METHOD FOR

HYPERSPECTRAL DATA BASED ON COMBINED SPECTRAL

SIGNATURESIGARSS 2011 Vancouver, 24-29 July

Xiao Fan, Ye Zhang, Feng Li, Yushi Chen, Tao Shao, Shuang Zhoufrom 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

SVDDtechniques

derivative

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

Train SVDD

Fusion Rule

Reflectance 1st Derivative

Test SVDD Test SVDD

Binary Recognition Result

Threshold

Discriminating Gray Value

Train SVDD

Discriminating Gray Value

Fused Discriminating Gray Value

Conclusions

Experiments & Results

Method & System

Techniques

Starting point

Content

Experimental Data

Asphalt

Meadows

Gravel

Trees

Metal sheets

Bare soil

Bitumen

Bricks

Shadow

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

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Asphalt

SAMSID

linear SVDD

poly 2 SVDD

poly 3 SVDDgauss SVDD

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Gravel

SAMSID

linear SVDD

poly 2 SVDD

poly 3 SVDDgauss SVDD

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Trees

SAMSID

linear SVDD

poly 2 SVDD

poly 3 SVDDgauss SVDD

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Metal sheet

SAMSID

linear SVDD

poly 2 SVDD

poly 3 SVDDgauss SVDD

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Bare soil

SAMSID

linear SVDD

poly 2 SVDD

poly 3 SVDDgauss SVDD

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Bitumen

SAMSID

linear SVDD

poly 2 SVDD

poly 3 SVDDgauss SVDD

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Bricks

SAMSID

linear SVDD

poly 2 SVDD

poly 3 SVDDgauss SVDD

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False AlarmP

rob.

of

Det

ectio

n

Shadow

SAMSID

linear SVDD

poly 2 SVDD

poly 3 SVDDgauss SVDD

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Meadows

SAMSID

linear SVDD

poly 2 SVDD

poly 3 SVDDgauss SVDD

Experiment 1

Area under the ROC curve with Pf from 0 to 1

Category

DetectorAsphalt Meadows Gravel Trees

Metalsheet

Bare soil Bitumen Bricks Shadow

SAM 0.896 0.8958 0.935 0.9598 0.9987 0.8095 0.97 0.9437 0.9441

SID 0.8999 0.8585 0.933 0.9559 0.9987 0.8042 0.9662 0.9432 0.9231

Linear 0.8978 0.8894 0.9345 0.959 0.9987 0.7987 0.97 0.9434 0.9558

Poly2 0.9505 0.8972 0.9322 0.9572 0.9996 0.7744 0.9852 0.9668 0.9983

Poly3 0.9505 0.8968 0.9322 0.9538 0.9996 0.7661 0.9852 0.9668 0.9983

Gaussian 0.9529 0.9309 0.9403 0.945 0.9994 0.8358 0.9869 0.9734 0.9987

Experiment 2

Illumination variation and terrain undulation make the spectral-amplitude fluctuation

derivative spectra vs reflective spectral

mean spectral variance

11 n

inv v i

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Asphalt

reflective

derivative

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Meadows

reflective

derivative

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Gravel

reflective

derivative

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Trees

reflective

derivative

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Metal sheet

reflective

derivative

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Bare soil

reflective

derivative

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Bitumen

reflective

derivative

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Bricks

reflective

derivative

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Shadow

reflective

derivative

Experiment 2

Area under the ROC curve with Pf from 0 to 1

Category Asphalt Meadows Gravel TreesMetalsheet

Bare soil Bitumen Bricks Shadow

variance(10-3)

13.057 4.5778 3.5456 2.8492 1.5595 1.2505 0.6827 0.3948 0.2574

reflective 0.9994 0.8358 0.945 0.9309 0.9529 0.9403 0.9734 0.9987 0.9869

derivative 0.9999 0.8367 0.9717 0.8611 0.9205 0.9026 0.8936 0.9678 0.9155

Experiment 3

Combined the two spectral signatures by different weights

Equal weights

Unequal weights

0.5r d

p

r p p

c

c v

0 0.005 0.010

0.2

0.4

0.6

0.8

1

variance

wei

ght

of r

efle

ctan

ce

equal

p=1

p=4

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Asphalt

worse

equal

p=1p=4

better

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Meadows

worse

equal

p=1p=4

better

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Gravel

worse

equal

p=1p=4

better

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Trees

worse

equal

p=1p=4

better

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Metal sheet

worse

equal

p=1p=4

better

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Bare soil

worse

equal

p=1p=4

better

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Bitumen

worse

equal

p=1p=4

better

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Bricks

worse

equal

p=1p=4

better

0 0.5 10

0.2

0.4

0.6

0.8

1

Prob. of False Alarm

Pro

b. o

f D

etec

tion

Shadow

worse

equal

p=1p=4

better

Experiment 3

Area under the ROC curve with Pf from 0 to 1

Category

DetectorAsphalt Meadows Gravel Trees

Metalsheet

Bare soil Bitumen Bricks Shadow

Worse 0.9994 0.8358 0.945 0.8611 0.9026 0.9205 0.8936 0.9678 0.9155

Equal 0.9998 0.8472 0.9572 0.9242 0.9439 0.9574 0.973 0.9976 0.9868

P=1 0.9999 0.8525 0.9576 0.9251 0.9442 0.9566 0.9734 0.998 0.9871

P=4 0.9999 0.8566 0.9591 0.9273 0.9437 0.9537 0.9738 0.9986 0.9872

Better 0.9999 0.8367 0.9717 0.9309 0.9403 0.9529 0.9734 0.9987 0.9869

Thank YouEmail: fancy_2626@163.com