On Automatic Absorption Detection for Imaging Spectroscopy: A Comparative Study

In this paper, we aim at presenting a survey on automatic absorption recovery methods for imaging spectroscopy. We commence by viewing the algorithms in the literature from a technical perspective and presenting an overview of the derivative analysis, fingerprint, and maximum modulus wavelet transform techniques. In addition to these methods, we also present a novel absorption recovery approach based upon unimodal regression and continuum removal. With this technical review of the methods under study, we perform a complexity analysis and examine the implementation issues pertaining to each of the alternatives. We show how detected absorption bands can be used for purposes of material identification. We conclude this paper by providing a performance study and providing identification results on hyperspectral imagery. To this end, we make use of a number of distance measures to evaluate the quality of the recovered absorptions, as compared to continuum-removed spectra.

[1]  Warren L. Butler,et al.  HIGHER DERIVATIVE ANALYSIS OF COMPLEX ABSORPTION SPECTRA , 1970 .

[2]  William A. P. Smith,et al.  Skin reflectance modelling for face recognition , 2004, ICPR 2004.

[3]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[4]  Q. Stout.  Optimal Algorithms for Unimodal Regression , 2000 .

[5]  Carle M. Pieters,et al.  Deconvolution of mineral absorption bands: An improved approach , 1990 .

[6]  R. Clark,et al.  Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications , 1984 .

[7]  Robert B. Ash,et al.  Information Theory , 2020, The SAGE International Encyclopedia of Mass Media and Society.

[8]  David A. Landgrebe,et al.  HYPERSPECTRAL DATA ANALYSIS AND FEATURE REDUCTION VIA PROJECTION PURSUIT , 1999 .

[9]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[10]  William D. Philpot,et al.  Yellowness index: An application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation , 1999 .

[11]  J. Hogg.  Quantitative remote sensing of land surfaces , 2004 .

[12]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[13]  Andrew P. Witkin,et al.  Scale-space filtering: A new approach to multi-scale description , 1984, ICASSP.

[14]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .

[15]  Susan L. Ustin,et al.  Investigating the Relationship Between Liquid Water and Leaf Area in Clonal Populus , 1998 .

[16]  M. Piech,et al.  Symbolic representation of hyperspectral data. , 1987, Applied optics.

[17]  L. Goddard.  Information Theory , 1962, Nature.

[18]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[19]  D. Sims,et al.  Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features , 2003 .

[20]  David A. Landgrebe,et al.  Toward an optimal supervised classifier for the analysis of hyperspectral data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Ronald L. Graham,et al.  An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set , 1972, Inf. Process. Lett..

[22]  David A. Landgrebe,et al.  Hyperspectral image data analysis , 2002, IEEE Signal Process. Mag..

[23]  Bruno O. Shubert,et al.  Random variables and stochastic processes , 1979 .

[24]  S. J. Sutley,et al.  Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems , 2003 .

[25]  P. Atkinson,et al.  Cokriging with airborne MSS imagery , 1994 .

[26]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

[27]  J. Franklin,et al.  Biomass distribution mapping using airborne digital video imagery and spatial statistics in a semi-arid environment , 1996 .

[28]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[29]  David G. Stork,et al.  Pattern Classification , 1973 .

[30]  David A. Landgrebe,et al.  Hyperspectral data analysis and supervised feature reduction via projection pursuit , 1999, IEEE Trans. Geosci. Remote. Sens..

[31]  Michael D. Steven,et al.  High resolution derivative spectra in remote sensing , 1990 .

[32]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[33]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[34]  IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 34. NO. 4, JULY 1996 Universal Multifractal Scaling of Synthetic , 1996 .

[35]  Piech Ma,et al.  Symbolic representation of hyperspectral data , 1987 .

[36]  H. D. Brunk,et al.  AN EMPIRICAL DISTRIBUTION FUNCTION FOR SAMPLING WITH INCOMPLETE INFORMATION , 1955 .

[37]  Edwin R. Hancock,et al.  Skin reflectance modelling for face recognition , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[38]  A. Goetz,et al.  Column atmospheric water vapor and vegetation liquid water retrievals from Airborne Imaging Spectrometer data , 1990 .

[39]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[40]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[41]  Adrian J. Brown.  Spectral curve fitting for automatic hyperspectral data analysis , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[42]  P. Gong,et al.  Spectral Feature Extraction of Hyperspectral Images Using Wavelet Transform , 2006 .

[43]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[44]  Aarnout Brombacher,et al.  Probability... , 2009, Qual. Reliab. Eng. Int..