Imaging spectroscopy for scene analysis: challenges and opportunities

In this study, the authors explore the opportunities, application areas and challenges involving the use of imaging spectroscopy as a means for scene understanding. This is important, since scene analysis in the scope of imaging spectroscopy involves the ability to robustly encode material properties, object composition and concentrations of primordial components in the scene. The combination of spatial and compositional information opens-up a vast number of application possibilities. For instance, spectroscopic scene analysis can enable advanced capabilities for surveillance by permitting objects to be tracked based on material properties. In computational photography, images may be enhanced taking into account each specific material type in the scene. For food security, health and precision agriculture it can be the basis for the development of diagnostic and surveying tools which can detect pests before symptoms are apparent to the naked eye. This combination of a broad domain of application with the use of key technologies makes the use of imaging spectroscopy a worthwhile opportunity for researchers in the areas of computer vision and pattern recognition.

[1]  Gudrun Klinker,et al.  A physical approach to color image understanding , 1989, International Journal of Computer Vision.

[2]  Ramesh Raskar,et al.  Introduction , 2006, SIGGRAPH Courses.

[3]  Theo Gevers,et al.  Detection and Classification of Hyper-Spectral Edges , 1999, BMVC.

[4]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[5]  Michael S. Brown,et al.  Interactive Visualization of Hyperspectral Images of Historical Documents , 2010, IEEE Transactions on Visualization and Computer Graphics.

[6]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, International Conference on Artificial Neural Networks.

[7]  I. Jolliffe Principal Component Analysis , 2002 .

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

[9]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[10]  Mark Berman Some Unmixing Problems and Algorithms in Spectroscopy and Hyperspectral Imaging , 2006, 35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06).

[11]  Shengcai Liao,et al.  Illumination Invariant Face Recognition Using Near-Infrared Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Michael Isard,et al.  Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[13]  Michael Brady,et al.  Feature-based correspondence: an eigenvector approach , 1992, Image Vis. Comput..

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

[15]  B. Hapke Theory of reflectance and emittance spectroscopy , 1993 .

[16]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.

[17]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

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

[19]  K. Torrance,et al.  Theory for off-specular reflection from roughened surfaces , 1967 .

[20]  Matthew A. Brown,et al.  Learning Local Image Descriptors , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  M. Lennon,et al.  Spectral unmixing of hyperspectral images with the independent component analysis and wavelet packets , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[22]  Alexei A. Efros,et al.  Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[23]  Rama Chellappa,et al.  Estimation of Illuminant Direction, Albedo, and Shape from Shading , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Sang Uk Lee,et al.  Shape from shading using graph cuts , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[25]  Richard G. Derwent,et al.  Radiative forcing in the 21st century due to ozone changes in the troposphere and the lower stratosphere , 2003 .

[26]  James E. Harvey,et al.  Modified Beckmann-Kirchoff scattering theory for nonparaxial angles , 1998, Optics & Photonics.

[27]  L. Itti,et al.  Search Goal Tunes Visual Features Optimally , 2007, Neuron.

[28]  Berthold K. P. Horn,et al.  Shape from shading , 1989 .

[29]  Jun Zhou,et al.  An affine Invariant hyperspectral texture descriptor based upon heavy-tailed distributions and fourier analysis , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[30]  Cong Phuoc Huynh,et al.  Shape and refractive index recovery from single-view polarisation images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  Glenn Healey,et al.  Invariant recognition in hyperspectral images , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[32]  Ali Shokoufandeh,et al.  Indexing using a spectral encoding of topological structure , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[33]  Michael Lindenbaum,et al.  On the Performance of Connected Components Grouping , 2004, International Journal of Computer Vision.

[34]  Elli Angelopoulou,et al.  Objective Colour from Multispectral Imaging , 2000, ECCV.

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

[36]  Cong Phuoc Huynh,et al.  Simultaneous photometric invariance and shape recovery , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[37]  Cong Phuoc Huynh,et al.  A Probabilistic Approach to Spectral Unmixing , 2010, SSPR/SPR.

[38]  Cong Phuoc Huynh,et al.  A Solution of the Dichromatic Model for Multispectral Photometric Invariance , 2010, International Journal of Computer Vision.

[39]  Michael J. Brooks,et al.  The variational approach to shape from shading , 1986, Comput. Vis. Graph. Image Process..

[40]  Stefan Rahmann,et al.  Reconstruction of specular surfaces using polarization imaging , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[41]  Nianjun Liu,et al.  Boosted Band Ratio Feature Selection for Hyperspectral Image Classification , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[42]  Cong Phuoc Huynh,et al.  Hyperspectral imaging for skin recognition and biometrics , 2010, 2010 IEEE International Conference on Image Processing.

[43]  Andrew Zisserman,et al.  A Visual Vocabulary for Flower Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[44]  Edwin R. Hancock,et al.  Recovery of Surface Height Using Polarization from Two Views , 2005, CAIP.

[45]  Nuno Vasconcelos,et al.  On the efficient evaluation of probabilistic similarity functions for image retrieval , 2004, IEEE Transactions on Information Theory.

[46]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[47]  Jun Zhou,et al.  MILIS: Multiple Instance Learning with Instance Selection , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[49]  David Lowe,et al.  A Generative Model for Separating Illumination and Reflectance from Images , 2003, J. Mach. Learn. Res..

[50]  J. Boardman,et al.  Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper (SAM) algorithm , 1992 .

[51]  Zhouyu Fu,et al.  Discriminant Absorption-Feature Learning for Material Classification , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[52]  Zhouyu Fu,et al.  Specular Free Spectral Imaging Using Orthogonal Subspace Projection , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

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

[54]  K. Sengupta,et al.  Using geometric hashing with information theoretic clustering for fast recognition from a large CAD modelbase , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[55]  Ronald A. Rensink The Dynamic Representation of Scenes , 2000 .

[56]  Edwin R. Hancock,et al.  Multi-view surface reconstruction using polarization , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

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

[58]  Philip H. S. Torr,et al.  The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix , 1997, International Journal of Computer Vision.

[59]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[60]  Katsushi Ikeuchi,et al.  Numerical Shape from Shading and Occluding Boundaries , 1981, Artif. Intell..

[61]  P. Beckmann,et al.  The scattering of electromagnetic waves from rough surfaces , 1963 .

[62]  O. Drbohlav,et al.  Unambiguous determination of shape from photometric stereo with unknown light sources , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[63]  Cong Phuoc Huynh,et al.  A NURBS-based spectral reflectance descriptor with applications in computer vision and pattern recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.