1 Dimensionality Reduction in Hyperspectral Image Analysis Using Independent Component Analysis...

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Dimensionality Reduction in Hyperspectral Image Analysis

Using Independent Component Analysis

Hongtao Du

Feb 18, 2003

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Status SummaryWhat is hyperspectral image?How to analyze hyperspectral

image?Why reduce dimensionality in

hyperspectral image analysis?

What is independent component analysis?

How to use ICA to reduce dimensionality in HSI?

Any parallel solution? Implement on FPGA?

In trod u c tio n on H yp e rsp e c tra l Im a ge

H yp e rspe c tra l Im a g e A n a lys is

In d ep e nd e nt C o m po ne n t A n a lys is

P a ra lle l ICA

Im p le m en ta tio n o n FP G A

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Electromagnetic Spectrum

Criterion: wavelength

[1]

E le c tro m ag n e tic S p ec trum

M u ltis pe c tra l Im a ge

H yp ersp e ctra l Im a ge



In d ep e nd e n t C o m po ne n t A n a lys is

P a ra lle l IC A


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Number of Bands in Spectral Images

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Multispectral Image

Simplest one: RGB imagesNot necessarily contiguousMultispectral sensor collect data

– Simultaneously– Sequentially


M u lt is pe c tra l Im a ge

H yp ers p e ctra l Im a ge


H yp e rs pe c tra l Im a g e A n a lys is


P a ra lle l IC A


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Hyperspectral Image

In narrow and contiguous wavelength bands.

Most Hyperspectral– 100~300 bands– Interval < 15 nm

Hyperspectral sensor collect data – Simultaneously


M u lt is pe c tra l Im a ge

H yp ers p e ctra l Im a ge


H yp e rs pe c tra l Im a g e A n a lys is


P a ra lle l IC A


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Spectral analysis

Soil Rock

VegetationWater


A n a lys is S p a ces

C h a lle n ge s a n d A p pro ach es



P a ra lle l IC A


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Analysis Spaces

Image Space Spectral Space

Feature Space


A n a lys is S p a c es

C h a lle n ge s a n d A p pro ac h es



P a ra lle l IC A


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Analysis Spaces (Cont’)

Image space– Pixels are displayed in grey scale.– Spatial analysis

Spectral space– Pixels are functions of wavelength.– Spectral analysis

Feature space– Pixels are points in N-dimensional space.– Relationships among Pixels

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Challenges and Approaches

M any Bands

M atrixFactorization

[2]

BandSelection

PrincipalCom ponent

Transform ation[3]

FeatureExtraction

LargeDim ensionality

Need M oreTrain ing p ixels

N_FINDER [4]

IdentifyingLabelingTrain ingSam ples

Feed Forw ardNeural Netw ork

[5]

Su itab leC lassifiers

C lassificatio nAccuracy

ResolutionNot H igh

ICA [6 ]

L inearUnm ixing

Pro jectionPursuit [7]

Pro jection

Pureness


A n a lys is S p a c es

C h a lle n ge s a n d A p pro ac h es



P a ra lle l IC A


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FastICA Algorithm

One Unit

Process

w

wxwgExwxgEw TT )}({)}({ '

www /

p

jjj

Tppp wwwww

1111

1111 / pTppp wwww

InitializeWeight Vector

Update

Normalize

Decorrelate

Normalize

Next

Multiple

Units

Decorrelation

Loop until

Converge

Loop until

Converge



F a s tICA

U s in g IC A to R e d uce D im e n s io n a lity

In d ep e nd e nt Co m po ne n t A n a lys is

P a ra lle l ICA


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Using ICA to Reduce Dimensionality



F a s tIC A

U s in g IC A to R e d uce D im e n s io n a lity


P a ra lle l IC A


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Parallel ICA

Internal

Decorrelation

jn

vjvjv

Tiuiuiu

1

p

viviv

Tiupipi

1)1()1(

jn

rjrjr

Tpi

p

viviv

Tpipipi

1)1(

1)1()1()1(

jnp

vvv

Tpipipi

1)1()1()1(

External

Decorrelation




D ia g ram

C o m pa rison

P a ra lle l ICA


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Parallel ICA Diagram




D ia g ram

C o m pa rison

P a ra lle l ICA


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Performance Comparison




D ia g ram

C o m pa rison

P a ra lle l ICA


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Advantage and Disadvantage of FPGA

Advantages– Speedup– Parallel computation

Disadvantages– Complexity– Size of data set




P a ra lle l ICA

A d va nta ge an d D isa d va n ta ge

S yn th es is P roce sses


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Synthesis ProcessesIn trod u c tio n on H yp e rsp e c tra l Im a ge



P a ra lle l ICA

A d va nta ge an d D isa d va n ta ge

S yn th es is P roce sses


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Conclusions

HSI and HSI AnalysisReduce dimensionalityICA Using ICA to reduce dimensionalityParallel ICAImplement on FPGA

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References1. The office of Biological & Physical Research, “Introduction of Space Research”,

NASA, http://spaceresearch.nasa.gov/general_info/light_06-2002_lite.html2. M. Velez-Reyes et al. “Comparison of matrix factorization algorithms for band

selection in hyperspectral imagery”. In SPIE 14th Annual International Symposium on Aerospace/Defense Sensing, Simulation and Controls, volume 4049(2000), pages 288–297, 2000.

3. P. Hsu and Y. Tseng. “Primary study of fourier spectrum feature extraction for hyperspectral image”. In The 19th Asian Conference on Remote Sensing, Manila, 16-20 November 1998. Asian Association of Remote Sensing (AARS).

4. E. Winter and M. Winter. “Autonomous hyperspectral end-member determination methods”. In EUROPTO Conference on Sensors, Systems, and Next-Generation Satellites V, volume 3870, pages 150–158, Florence, Italy, September 1999.

5. S. Subramanian, et al. “Methodology for hyperspectral image classification using novel neural network”. In A.Evan Iverson and Sylvia S. Shen, editors, SPIE: Algorithms for Multispectral and Hyperspectral Imagery III, volume 3071, pages 128–137, Orlando, FL, USA, April 1997.

6. L. Parra and S. Sajda. “Unmixing hyperspectral data”, Advances in Neural Information Processing Systems 12, MIT Press, pp. 942-948, 2000

7. S. Chiang, et al. “Unsupervised hyperspectral image analysis using independent component analysis”. In Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000, volume 4, pages 3136 – 3138, Honolulu, HI, USA, 24-28 July 2000. IEEE.

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