1 / 12

Towards streaming hyperspectral endmember extraction

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

cutter
Download Presentation

Towards streaming hyperspectral endmember extraction

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Towards streaming hyperspectral endmember extraction Dževdet Burazerović, Rob Heylen, Paul Scheunders IBBT-Visionlab, University of Antwerp, Belgium IGARSS 2011 July 24-29, Vancouver, Canada

  2. Outline Prior art and motivation LMM, N-findr The proposed algorithm Distance-based simplex formulation Streaming endmember estimation Experiments and results Conclusions

  3. 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

  4. 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

  5. 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

  6. 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

  7. Distance-based simplex formulation • ViaCayley-Menger determinant, Schur complement e4 e3 V3 e1 e2

  8. 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

  9. 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

  10. Cluster validation Results with N-findr, on Cuprite i = 9(13 spectra) i = 7

  11. 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)

  12. 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

More Related