Hyperspectral images of natural scenes 2004


Scenes

The eight hyperspectral images available here were acquired from the same sites as those illustrated in Figure 1 of Foster, Nascimento, and Amano, 2004, and are a representative sample of the 25 images used in that study.  They consisted of a mixture of rural scenes from the Minho region of Portugal, containing, rocks, trees, leaves, grass, and earth and urban scenes from the cities of Porto and Braga, Portugal.  Images were obtained during the summers of 2002 and 2003, almost always under a clear sky. Particular care was taken to avoid scenes containing movement.  Scenes were illuminated by direct sunlight in clear or almost clear sky.  Colour pictures of the eight representative scenes are shown here, and full-size versions (each about 4MB) can be downloaded by clicking on each.

The estimated reflectance spectra (effective spectral reflectances) at each pixel in each of these example scenes can be downloaded as a WinZip file by clicking on the link at the bottom of each picture.  Each zipped file contains contains for each scene a Matlab scene-reflectances file (size e.g. 1018 x 1339 x 33), a Matlab radiance-by-reflectances file for converting reflectances to radiances (33 x 2), a Matlab reflectances file for the Munsell surface in the scene (33 x 2), a bright BMP image of the scene, and a Microsoft RTF information file.  The version of Matlab used to save these files was 7.2.0.232 (R2006a).  Details of the hyperspectral system, image acquisition, and processing are given further down the page.

If you use these images, please cite this source publication: Foster, D.H., Amano, K., Nascimento, S.M.C., & Foster, M.J. (2006). Frequency of metamerism in natural scenes. Journal of the Optical Society of America A, 23, 2359-2372.

Time-lapse hyperspectral radiance images, taken at intervals across the day, are available here

Scene 1
Scene 2
Scene 3
Scene 4

Scene 1

Download 386MB zip file
(1018x1339x33
Matlab array with edited version and supporting files)

Scene 2

Download 398MB zip file
(1017x1338x33
Matlab array with edited version and supporting files)

Scene 3

Download 361MB zip file
(1018x1267x33
Matlab array with edited version and supporting files)

Scene 4

Download 188MB zip file
(1019x1337x33
Matlab array with supporting files)

Scene 5
Scene 6
Scene 7
Scene 8

Scene 5

Download 380MB zip file
(1020x1339x32
Matlab array with edited version and supporting files)

Scene 6

Download 183MB zip file
(1021x1338x33
Matlab array with supporting files)

Scene 7

Download 180MB zip file
(1017x1340x33
Matlab array with supporting files)

Scene 8

Download 177MB zip file
(1018x1340x33
Matlab array with supporting files)

 


Details of hyperspectral system, image acquisition, and processing

The present system used a low-noise Peltier-cooled digital camera providing an x-y spatial resolution of 1344 x 1024 pixels (Hamamatsu, model C4742-95-12ER, Hamamatsu Photonics K. K., Japan) with a fast tunable liquid-crystal filter (VariSpec, model VS-VIS2-10-HC-35-SQ, Cambridge Research & Instrumentation, Inc., MA) mounted in front of the lens, together with an infrared blocking filter. Focal length was typically set to 75 mm and aperture to f/16 or f/22 to achieve a large depth of focus. The line-spread function of the system was close to Gaussian with standard deviation approx. 1.3 pixels at 550 nm. The intensity response at each pixel, recorded with 12-bit precision, was linear over the entire dynamic range. The peak-transmission wavelength was varied in 10-nm steps over 400720 nm and the bandwidth (FWHM) was 10 nm at 550 nm, decreasing to 7 nm at 400 nm and increasing to 16 nm at 720 nm. The acceptance angle of the camera was approx. 6 deg of visual angle.

Before image acquisition, the exposure at each wavelength was determined by an automatic routine so that maximum pixel output was within 8690% of saturation. Immediately after acquisition, the reflected spectrum from a small flat grey (Munsell N5 or N7) reference surface in the scene was recorded with a telespectroradiometer (SpectraColorimeter, PR-650, Photo Research Inc., Chatsworth, CA), the calibration of which was traceable to the National Physical Laboratory.

The raw images acquired by the hyperspectral camera were corrected only for dark noise, spatial nonuniformities (mainly off-axis vignetting), stray light, and any wavelength-dependent variations in magnification or translation.  For all scenes, the signal at 400 nm was relatively noisy, but data for this wavelength were not excluded.  For Scene 5, however, the signal at 720 nm was excessively noisy, and data for this wavelength were excluded, so the size of the scene-reflectances file is 1020 1339 32 (in any event, the signals at 400 and 720 nm make relatively small contributions to the in-vivo response of human cones to natural reflected spectra).  

Edited versions (labelled "_lax") have also been included for Scenes 1, 2, 3, and 5.  These edited versions have been corrected for some nonlinear chromatic artefacts arising from movement of foliage within the scene during image acquisition. 

Effective spectral reflectances at each pixel were estimated by normalizing the corrected signal against that obtained from the grey reference surface in the scene.  An informal description  of effective spectral reflectances and the effective global illuminant are given below. Further technical details are given in Foster et al. (2006), the latter including additional calibration data (p. 2360) and an analysis of effective spectral reflectances with direct and indirect illumination (Appendix A, pp. 2370-2371).

These data are for personal use only.  Acknowledgement of the relevant source publications should be given in any published work arising from these data: Foster, D.H., Amano, K., Nascimento, S.M.C., & Foster, M.J. (2006). Frequency of metamerism in natural scenes. Journal of the Optical Society of America A, 23, 2359-2372.


Notes on effective spectral reflectances and effective global illuminant

The effective spectral reflectance at  each pixel was obtained by dividing  the spectral radiance recorded by the hyperspectral camera at that position and wavelength by the spectral radiance recorded from a calibrated neutral reference surface (a Munsell grey) in the scene and then multiplying by the known spectral reflectance of the reference surface. The reference surface was usually flat and placed vertically facing the camera. Because surfaces oriented at an angle to the camera may reflect more light than vertical surfaces, their effective spectral reflectances may exceed unity.

There are three ways to deal with this.

1. If there is a Munsell grey sphere in the scene with a visible highlight, then multiply (wavelength-by-wavelength) the effective spectral reflectance at each pixel by the effective spectral reflectance at the flat reference and then divide by the effective spectral reflectance at the highlight. If there is a true specular highlight in the scene, e.g. from water (Scene 4, top right-hand corner), then this might instead be used as the divisor or it can be removed altogether from the calculation.

2. Divide all the effective spectral reflectances in the scene by a constant equal to the maximum effective spectral reflectance evaluated over all pixels and wavelengths.

3. Ignore it. The effective spectral reflectances are normally used to calculate a reflected spectrum. Multiplying the effective spectral reflectance by the data in the radiance-by-reflectances file recovers the original spectral radiance recorded by the camera. Multiplying instead by a new different illuminant spectrum will simulate the effect of replacing the original illuminant by this new illuminant. Thus there is a trade-off between decreasing all the spectral reflectances in the scene by a constant factor, as in method 2, and increasing the illuminant spectrum by the same factor.

The spectrum of the effective global illuminant was obtained by taking the actual reflected spectrum from a calibrated neutral reference surface (a Munsell grey) in the scene recorded with a telespectroradiometer and dividing by the known spectral reflectance of that surface. The spectral radiance recorded by the hyperspectral camera at each position may then interpreted as a product of this effective global illuminant and the effective spectral reflectance at that position.

Further technical details are given in Foster et al. (2006), particularly Appendix A, pp. 2370-2371.


Tutorial on transforming hyperspectral images

Click here for a tutorial on transforming hyperspectral image reflectances into reflected radiances and RGB colour images.

The tutorial includes sample code (MATLAB, The MathWorks Inc) and technical notes on the interpretation of reflectance spectra from natural scenes. Also available for download is a zipped package containing a small test hyperspectral image, daylight illuminant spectra, and a conversion routine for producing sRGB images.


(c) D. H. Foster, S. M. C. Nascimento, and K. Amano, February 2012

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