Edge-Preserving Image Smoothing Constraint in Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) of Hyperspectral Data

Abstract

This article explores smoothing with edge-preserving properties as a spatial constraint for the resolution of hyperspectral images with multivariate curve resolution–alternating least squares (MCR-ALS). For each constrained component image (distribution map), irrelevant spatial details and noise are smoothed applying an L₁- or L₀-norm penalized least squares regression, highlighting in this way big changes in intensity of adjacent pixels. The feasibility of the constraint is demonstrated on three different case studies, in which the objects under investigation are spatially clearly defined, but have significant spectral overlap. This spectral overlap is detrimental for obtaining a good resolution and additional spatial information should be provided. The final results show that the spatial constraint enables better image (map) abstraction, artifact removal, and better interpretation of the results obtained, compared to a classical MCR-ALS analysis of hyperspectral images.

Keywords

Hyperspectral image analysis multivariate curve resolution–alternating least squares MCR-ALS constraint spatial smoothing edge preservation

Get full access to this article

View all access options for this article.

References

Iordache

M.-D.

Bioucas-Dias

Plaza

“Sparse Unmixing of Hyperspectral Data”. IEEE T. Geosci. Remote. 2011. 49(6): 2014–2039.

Matikainen

Karila

“Segment-Based Land Cover Mapping of a Suburban Area—Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points”. Remote Sens. 2011. 3(8): 1777–1804.

Hay

G.J.

Kyle

Hemachandran

Chen

et al.

“Geospatial Technologies to Improve Urban Energy Efficiency”. Remote Sens. 2011. 3(8): 1380–1405.

M.-D. Iordache, A. Plaza, J. Bioucas-Dias. “On the Use of Spectral Libraries to Perform Sparse Unmixing of Hyperspectral Data”. Proc. Second IEEE Worskshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 2010. 1: 1–4. DOI: 10.1109/WHISPERS.2010.5594888.

Chuvieco

Kasischke

E.S.

“Remote Sensing Information for Fire Management and Fire Effects Assessment”. J. Geophys. Res. 2007. 112(G1): G01S90.

Liew

S.C.

Kwoh

L.K.

Lim

O.K.

Lim

“Remote Sensing of Fire and Haze”. In: Eaton

Radojevic

(eds). Forest Fires and Regional Haze in Southeast Asia, New York: Nova Science Publishers, 2001, pp. 67–89.

Leblon

Bourgeau-Chavez

San-Miguel-Ayanz

“Use of Remote Sensing in Wildfire Management”. In: Curkovic

(ed). Sustainable Development–Authoritative and Leading Edge Content for Environmental Management, Rijeka, Croatia: Intech, 2012, pp. 55–82.

Tauler

Smilde

Kowalski

“Selectivity, Local Rank, Three-Way Data Analysis and Ambiguity in Multivariate Curve Resolution”. J. Chemom. 1995. 9(1): 31–58.

Tauler

“Multivariate Curve Resolution Applied to Second Order Data”. Chemom. Intell. Lab. Sys. 1995. 30(1): 133–146.

10.

Ruckebusch

Blanchet

“Multivariate Curve Resolution: A Review of Advanced and Tailored Applications and Challenges”. Anal. Chim. Acta. 2007. 765: 28–36.

11.

Jaumot

De Juan

Tauler

“MCR-ALS GUI 2.0: New Features and Applications”. Chemom. Intell. Lab. Syst. 2015. 140: 1–12.

12.

De Juan

Tauler

“Multivariate Curve Resolution (MCR) from 2000: Progress in Concepts and Applications”. Crit. Rev. Anal. Chem. 2006. 36(3–4): 163–176.

13.

Duponchel

Elmi-Rayaleh

Ruckebush

Huvenne

J.-P.

“Multivariate Curve Resolution Methods in Imaging Spectroscopy: Influence of Extraction Methods and Instrumental Perturbations”. J. Chem. Inf. Model. 2003. 43(6): 2057–2067.

14.

De Juan

Tauler

Dyson

Marcolli

et al.

“Spectroscopic Imaging and Chemometrics: A Powerful Combination for Global and Local Sample Analysis”. TrAC, Trends Anal. Chem. 2004. 23(1): 70–79.

15.

Hugelier

Devos

Ruckebusch

“On the Implementation of Spatial Constraints in Multivariate Curve Resolution Alternating Least Squares for Hyperspectral Image Analysis”. J. Chemom. 2015. 29(10): 557–561.

16.

E.W. Bethel, O. Rübel, Prabhat, K. Wu, et al. “Modern Scientific Visualization Is More Than Just Pretty Pictures”. Technical Report LBNL-1450E, Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA (US), 2008. http://escholarship.org/uc/item/3ff8x5zt [accessed 15 Sep 2017].

17.

Brodlie

K.W.

Carpenter

L.A.

Earnshaw

R.A.

Gallop

J.R.

et al.

Scientific Visualization: Techniques and Applications, Dordrecht: Springer Science & Business Media, 2012.

18.

De Rooi

J.J.

Devos

Sliwa

Ruckebusch

et al.

“Mixture Models for Two-Dimensional Baseline Correction, Applied to Artifact Elimination in Time-Resolved Spectroscopy”. Anal. Chim. Acta. 2013. 771: 7–13.

19.

Jia

“Image Smoothing Via L0 Gradient Smoothing”. ACM T. Graphic. 2011. 30(5): 174–185.

20.

Kang

Lee

Chui

C.K.

“Flow-Based Image Abstraction”. IEEE Trans. Vis. Comput. Graphics. 2009. 15(1): 62–76.

21.

Kyprianidis

J.E.

Döllner

“Image Abstraction by Structure Adaptive Filtering”. EG UK Theory and Practice of Computer Graphics (TPCG). 2008, pp. 51–58. http://www.kyprianidis.com/p/tpcg2008/jkyprian-tpcg2008.pdf [accessed 15 Sep 2017].

22.

Rudin

L. I.

Osher

Fatemi

“Nonlinear Total Variation Based Noise Removal Algorithms”. Phys. D. (Amsterdam, Netherlands). 1992. 60(1–4): 259–268.

23.

M. Kass, J. Solomon. “Smoothed Local Histogram Filters”. In: Proceedings of the ACM SIGGRAPH. Los Angeles, CA: July 26-30, 2010. 29(4): Article 100.

24.

J. Tumblin, G. Turk. “LCIS: A Boundary Hierarchy for Detail-Preserving Contrast Reduction”. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques. New York, USA: ACM Press/Addison-Wesley Publishing Co., 1999. pp. 83–90.

25.

Paris

Durand

“A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach”. Eur. Conf. Comp. Vision. 2006. 4: 568–580.

26.

Chambolle

Caselles

Cremers

Novaga

et al.

“An Introduction to Total Variation for Image Analysis”. In: Fornasier

(ed). Theoretical Foundations and Numerical Methods for Sparse Recovery 9, Berlin, Germany: De Gruyter, 2010, pp. 263–340.

27.

Pomareda

Calvo

Pardo

Marco

“Hard Modeling Multivariate Curve Resolution Using LASSO: Application to Ion Mobility Spectra”. Chemom. Intell. Lab. Syst. 2010. 104(2): 318–332.

28.

Frasso

Eilers

P.H.C.

“L- and V-Curves for Optimal Smoothing”. Stat. Model. 2015. 15(1): 91–111.

29.

A. De Juan, R. Tauler. “Multivariate Curve Resolution–Alternating Least Squares for Spectroscopic Data”. In: C. Ruckebusch, editor. Data Handling in Science and Technology–Resolving Spectral Mixtures. Amsterdam: Elsevier, 2016. Pp. 5–52.

30.

Maeder

“Evolving Factor Analysis for the Resolution of Overlapping Chromatographic Peaks”. Anal. Chem. 1987. 59(3): 527–530.

31.

Windig

Guilment

“Interactive Self-Modeling Mixture Analysis”. Anal. Chem. 1991. 63(14): 1425–1432.

32.

Hugelier

Devos

Ruckebusch

“Constraining Shape Smoothness in Multivariate Curve Resolution–Alternating Least Squares”. J. Chemom. 2015. 29(8): 448–456.

33.

Hugelier

Firmani

Devos

Moreau

et al.

“Weighted Fuzzy Clustering for (Fuzzy) Constraints in Multivariate Image Analysis–Alternating Least Square of Hyperspectral Images”. J. Spectr. Imag. 2016. Article A7. https://www.impopen.com/jsi-abstract/I05_a7 [accessed Sep 15 2017].

34.

Hugelier

Piqueras

Bedia

De Juan

et al.

“Application of Sparseness Constraints in Multivariate Curve Resolution–Alternating Least Squares”. Anal. Chim. Acta. 2017. DOI:10.1016/J.Aca.2017.08.021.

35.

Green

R.O.

Eastwood

M.L.

Sarture

C.M.

Chrien

T.G.

et al.

“Imaging Spectroscopy in the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)”. Remote Sens. Environ. 1998. 65(3): 227–248.

36.

Universidad Pais Vasco. “Hyperspectral Remote Sensing Scenes”. 2014. http://www.ehu.eus/ccwintco/index.php?title=hyperspectral_remote_sensing_scenes [accessed May 3 2017].

37.

Amigo

J.M.

Babamoradi

Elcoroaristizabal

“Hyperspectral Image Analysis. A Tutorial”. Anal. Chim. Acta. 2015. 896: 34–51.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.90 MB