Design goals and solutions for display of hyperspectral images

Design goals and solutions are proposed for the display of hyperspectral imagery on tristimulus displays. The requirements of a hyperspectral visualization depend on the task. We focus on creating consistent representations of hyperspectral data that can facilitate understanding and analysis of hyperspectral scenes, and may be used in conjunction with task-specific visualizations. Fixed linear spectral weighting envelopes are given which create natural looking imagery where hue, brightness, saturation and white-point have meanings consistent with the human visual system interpretation of natural scenes. For AVIRIS images, hue interpretation of water and vegetation is also preserved. The proposed designs avoid the pre-attentive distractions of PCA imagery, and provide comparable spectral and edge discriminability.

[1]  H. John Caulfield.  Nature's alternative to hyperspectral imaging and why nature is right , 2002, Optics + Photonics.

[2]  Luciano Alparone,et al.  Image fusion—the ARSIS concept and some successful implementation schemes , 2003 .

[3]  Arthur Petrosian,et al.  Wavelets in Signal and Image Analysis , 2001, Computational Imaging and Vision.

[4]  Wallace M. Porter,et al.  The airborne visible/infrared imaging spectrometer (AVIRIS) , 1993 .

[5]  J. Scott Tyo,et al.  Colorimetric representations for use with polarization-difference imaging of objects in scattering media , 1998 .

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

[7]  Wesley E. Snyder,et al.  Band selection using independent component analysis for hyperspectral image processing , 2003, 32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings..

[8]  Gaurav Sharma,et al.  Digital color imaging , 1997, IEEE Trans. Image Process..

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

[10]  P. Wintz,et al.  Information Extraction, SNR Improvement, and Data Compression in Multispectral Imagery , 1973, IEEE Trans. Commun..

[11]  Michael H. Brill,et al.  Color appearance models , 1998 .

[12]  S BoothKellogg,et al.  Visualizing real-time multivariate data using preattentive processing , 1995 .

[13]  James T. Enns,et al.  Visualizing real-time multivariate data using preattentive processing , 1995, TOMC.

[14]  Steven K. Rogers,et al.  Perceptual-based image fusion for hyperspectral data , 1997, IEEE Trans. Geosci. Remote. Sens..

[15]  J. Scott Tyo,et al.  Principal-components-based display strategy for spectral imagery , 2003, IEEE Trans. Geosci. Remote. Sens..

[16]  Philip K. Robertson,et al.  The application of perceptual color spaces to the display of remotely sensed imagery , 1988 .

[17]  UL Ao.  Picture problem , 1993, In Practice.

[18]  N. Canagarajah,et al.  Wavelets for Image Fusion , 2001 .

[19]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[20]  Mark D. Fairchild,et al.  Color Appearance Models , 1997, Computer Vision, A Reference Guide.

[21]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[22]  Gerrit Polder,et al.  Visualization of spectral images , 2001, International Symposium on Multispectral Image Processing and Pattern Recognition.

[23]  Edward R. Tufte,et al.  Envisioning Information , 1990 .

[24]  S. J. Sutley,et al.  USGS Digital Spectral Library splib05a , 2003 .

[25]  Edward Tufte,et al.  Visual Explanations , 1997 .

[26]  Maureen C. Stone,et al.  A field guide to digital color , 2003 .