towards streaming hyperspectral endmember extraction n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Towards streaming hyperspectral endmember extraction PowerPoint Presentation
Download Presentation
Towards streaming hyperspectral endmember extraction

Loading in 2 Seconds...

play fullscreen
1 / 12

Towards streaming hyperspectral endmember extraction - PowerPoint PPT Presentation


  • 127 Views
  • Uploaded on

Towards streaming hyperspectral endmember extraction. D ž evdet Burazerović , Rob Heylen , Paul Scheunders IBBT-Visionlab, University of Antwerp, Belgium IGARSS 2011 July 24-29 , Vancouver, Canada. Outline. Prior art and motivation LMM, N- findr The proposed algorithm

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Towards streaming hyperspectral endmember extraction' - cutter


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
towards streaming hyperspectral endmember extraction
Towards streaming hyperspectral endmember extraction

Dževdet Burazerović, Rob Heylen, Paul Scheunders

IBBT-Visionlab, University of Antwerp, Belgium

IGARSS 2011

July 24-29, Vancouver, Canada

outline
Outline

Prior art and motivation

LMM, N-findr

The proposed algorithm

Distance-based simplex formulation

Streaming endmember estimation

Experiments and results

Conclusions

linear mixture model
Linear mixture model

An observed spectrum xis a (constrained) linear sum of pendmember (EM) spectra ei:

  • Then, EMs = vertices of the largest (p-1)-dim. simplex enclosing (most of) the x:

e4

e3

x

e2

e1

n findr
N-findr
  • Estimates the largest simplex via repetitive vertex replacements
    • “single replacement” (SR) vs. “best replacement” (BR)
    • “single iteration” (SI) vs. “full iteration” (FI)

3

1

1

2

2

Random initial

No replacement

Replacement

motivation
Motivation
  • Finding the largest simplex is not sufficient/necessary (in real data, un-supervised scenarios)
  • Worthwhile to seek efficient implementations

(*)

(*) S. Dowler, M. Andrews: “On the convergence of N-findr …”, IEEE GRS Letters, 2011

the proposed algorithm
The proposed algorithm
  • Extract EMs in1-pass, streaming (online) fashion
    • Reformulate the simplex-vol. measurement to avoid dim. red.
    • Grow a suitable initial simplex for a given # of EMs
    • Maximize this simplex by subsequent replacements (N-findr)

ep

image

normally, n > p

distance based simplex formulation
Distance-based simplex formulation
  • ViaCayley-Menger determinant, Schur complement

e4

e3

V3

e1

e2

growing the initial simplex
Growing the initial simplex
  • Use empirical CDFs to set thresh. for the simplex-vol. increment
  • E.g., add xk as p-th EM, if FP(Vk/VP-1)≥0.5

h

h ~ V4/V3

V3

1

0

comparison setup
Comparison setup
  • Acknowledge the variability of both algorithms
    • Streaming: threshold function for growing the initial simplex
    • N-findr: random selection of the initial simplex (EMs)
  • Compare results (EMs) from multiple runs
  • Use cluster validation to determine consistent EMs

M – EMs

K – runs

M x K – data points

cluster validation
Cluster validation

Results with N-findr, on Cuprite

i = 9(13 spectra)

i = 7

comparison results
Comparison results
  • Ground truth:P EM-cluster centroidsfrom ~40 runs of N-findr
  • Test data: P EMs from a single streaming pass
  • Classification: N-Neighbor + visual comparison of the spectra
  • Accuracy: 13/18 (72.2%) on Cuprite, 4/7 (71.4%) on M.F.

Cuprite, P=18(350 x 350 x 188)

Moffet Field, P=7 (335 x 370 x 56)

conclusions
Conclusions
  • The use of dist.-based simplex formulation enables a new paradigm of EM-extraction:
    • A streaming (online) implementation based on N-findr
    • Avoiding the need to pre-load the entire image into memory
  • Tested on diverse data, finds most of the EMs that are found by repetition of the reference methods (N-findr)
  • Possible extension to other strategies for streaming-based simplex estimation and measurement