This study investigated appropriate methodologies for displaying hyperspectral imagery based on knowledge of human color vision as applied to Hyperion and AVIRIS data. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were used to reduce the data dimensionality, and these two methods were chosen also because of their underlying relationships to the opponent color model of human color perception. PCA and ICA-based strategies were then explored by mapping the first three PC or IC to several opponent color spaces including CIELAB, HSV, YCbCr, and YIQ. The gray world assumption, which states that given an image with sufficient amount of color variations, the average color should be gray, was used to set the mapping origins. The rendered images are well color balanced and can offer a first look capability or initial classification for a wide variety of spectral scenes. I

Dave Messinger

Ethan Montag

Hongqin Zhang

CiteSeerX

 
 
CIS-Kodak New Collaboration Research  
Grant Report 
 
 
Visualization of High-dimensional Remote-Sensing  
Data Products 
 
 
 
 
 
 
 
 
Hongqin Zhang, Ethan Montag, Dave Messinger 
Chester F. Carlson Center for Imaging Science 
Rochester Institute of Technology 
Rochester, NY, USA 
 
 
 
 
 
 
 
October 2005 
 
 
 
 
 
 
 
 
 Abstract 
 
This study investigated appropriate methodologies for displaying hyperspectral imagery based 
on  knowledge  of  human  color  vision  as  applied  to  Hyperion  and  AVIRIS  data.  Principal 
Component Analysis (PCA) and Independent Component Analysis (ICA) were used to reduce 
the data dimensionality, and these two methods were chosen also because of their underlying 
relationships  to  the  opponent  color  model  of  human  color  perception.  PCA  and  ICA-based 
strategies  were then explored by  mapping the first three PC  or IC  to  several  opponent color 
spaces including CIELAB, HSV, YCbCr, and YIQ. The gray world assumption, which states that 
given an image with sufficient amount of color variations, the average color should be gray, was 
used to set the mapping origins. The rendered images are well color balanced and can offer a first 
look capability or initial classification for a wide variety of spectral scenes.  
 
I. Introduction 
 
Hyper-Spectral imagers that can measure spectra at more than 200 narrow, contiguous bands 
have been developed in recent years.  With the increasing number of bands, the remotely sensed 
hyperspectral  imagery  provides  finer  spectral  resolution,  yet  presents  challenges  for  data 
visualization techniques for display of the information content of such datasets. Color, as one of 
the attributes to display information, may be used effectively for more efficient data presentation 
and interpretation.  
The  goal  of  this  research  is  to  investigate  the  application  of  color  and  human  vision 
system  in  the  visualization  of  high-dimensional  remote  sensing  products  and  develop 
visualization  techniques  based  on  principles  of  human  vision,  color  appearance,  and  color 
perception for more efficient data exploitation and improved data mining. 
 
II. Background 
 
How can the hyperspectral information from a portion of the spectrum not perceived by humans 
be presented in a manner that is more easily understood? At first, in order to display the data on 
a standard tristimulus display, the dimensionality of the data bands must be reduced to three or less.  Traditionally,  one  can  map  three  widely  spaced  bands  to  RGB  in  hopes  that  they  are 
minimally correlated or choose three specific bands to highlight a particular spectral feature, for 
example, the absorption peak of water. In addition to the popular simple methods, however, data 
projection is one of the common visual ways to get the interesting subsets of the original data by 
projecting high-dimensional data to lower dimensions. Principal Component Analysis (PCA) and 
Independent Component Analysis (ICA) are two widely used methods for such a purpose. In this 
study, these two methods were used to reduce the data dimensionality in order to make the data 
more amenable to visualization in three-dimensional color space. In addition, these two methods 
were chosen because of their underlying relationships to the opponent color model of human 
color perception.  
Hyperspectral  data  bands  are  often  highly  correlated.  The  Principal  Component  (PC) 
Transformation can be used to produce uncorrelated output bands. This is done by finding a new 
set of orthogonal axes that have their origin at the data mean and that are rotated so the data 
variance is maximized. PC bands are linear combinations of the original spectral bands and are 
uncorrelated. Unlike the intensity distribution in any particular band, the PCs provide spectral 
information spanning the entire spectral range of interest, preventing any one feature from being 
completely  missed.  Generally,  the  first  3  PC  bands  can  explain  more  than  90%  of  the  entire 
variance. While a common method for presenting PC images in a pseudocolor display is to map 
the  first  three  PCs  into  an  RGB  image,  a  certain  amount  of  research  has  been  performed  on 
perceptually  based  color  displays  for  use  with  scientific  data  [Tyo,  etc.,  Beauchemin,  etc.]. 
Incorporating knowledge of human color processing into display strategies for spectral imagery 
was thought to be helpful in order to create images that present the rich information available 
from spectral sensors in a readily interpretable manner. 
It is well known that the human vision system processes color by means of an achromatic 
channel and two opponent-color channels, which is called the opponent-color model of human 
color perception. It was demonstrated that the achromatic, red-green opponent, and blue-yellow 
opponent-color  processing  channels  represent  statistically  noncovariant  information  pathways 
derivable from a PC analysis of the spectral sensitivities of the three classes of photoreceptors 
[Buchsbaum and Gottschalk, 1983]. Their result was the first mathematical explanation for why the human vision system processes color via opponent channels. Therefore, the opponent color 
formulation can be used  to develop a  display  strategy for high-dimensional spectral imagery 
based on a similar PC analysis. 
  ICA is a statistical and computational technique that extracts independent source signals 
by searching for a linear or nonlinear transformation which minimizes the statistical dependence 
between components [Du, et al.]. It finds a nonorthogonal coordinate system in any multivariate 
data  that  minimizes  mutual  information  among  the  axial  projections  of  the  input  data.  The 
directions of the axes of this coordinate system are determined by both second-order and higher-
order statistics of the original data. The goal of ICA is to perform a transform that makes the 
resulting source outputs as statistically independent of each other as possible.  
ICA  was  first  proposed  by  Pierre  Comon  in  1994,  and  has  been  used  in  a  variety  of 
applications such as blind source separation, recognition, etc. As opposed to an orthogonal PCA 
basis, ICA has also been  considered as a method  to analyze natural  scenes.  Much interesting 
research  have  been  conducted  to  investigate  the  spatial  and  chromatic  structures  of  natural 
scenes  by  decomposing  the  spectral  images  into  a  set  of  linear  basis  functions  using  ICA. 
Wachtler,  et  al.  [Wachtler]  applied  ICA  to  hyperspectral  images  to  learn  an  efficient 
representation of color in natural scenes. Their founding suggests that nonorthogonal opponent 
encoding  of  photoreceptor  signals  leads  to  higher  coding  efficiency  and  is  a  result  of  the 
properties of natural spectra. ICA may be used to reveal the underlying statistical properties of 
color information in natural scenes.  
III. PCA and ICA processing  
 
Imagery from the Hyperion
6 sensor on the Earth Observing 1 (EO-1) spacecraft was used as the 
initial data set for this project. This sensor can image a 7.5 km by 100 km land area per image in 
220 spectral bands ranging from 0.4 to 2.5 µm with a 30-meter resolution per pixel. An urban
7 
image of an area near San Francisco was analyzed using techniques developed by co-PI David 
Messinger  to  create  15  abundance  maps.  The  raw  radiance  data  have  been  converted  to 
reflectance  through  an  atmospheric  compensation  scheme  to  aid  the  un-mixing  process. Additional imagery is available from the Hyperion site as well as other imagery provided by 
DIRS. 
  In the PCA and ICA processing step, the raw radiance data with removal of bad bands 
was used, and another Nevada scene from AVIRIS sensor with 50 bands selected (from band 172 
to  221)  was  also  tested.  These  datasets  were  designated  as  SanFran_196b,  SanFran_100b,  and 
Nevada_50b. The PCA and ICA results of these datasets are listed in the following tables. 
 
1.  SanFran_196b_PCA 
 
 
The 1st 3 PC (basis vectors) 
 
 
PC1 -> 74.56%, PC2 -> 21.54%, 
PC3 -> 1.95% 
Total by the 1st 3 PCs -> 98.05% 
 
 
Scatter plots of the data projection onto 
the 1st 3 PC grouped by the K-means 
classifier results (5 classes) 
 
 
Scatter plots of the data projection onto 
the 1st 3 PC grouped by Dave’s unmixing 
classifier results (15 classes) 
 
 
 
 
  
2.  SanFran_196b_ICA  
 
 
The 1st 3 Basis Vector of ICA 
 
3-D scatter plot of the 1st 3 IC data 
grouped by K-means classifier results (5 
classes) 
 
Scatter plots of the data projection onto 
the 1st 3 IC grouped by the K-means 
classifier results (5 classes) 
 
Scatter plots of the data projection onto 
the 1st 3 IC grouped by Dave’s unmixing 
classifier results (15 classes) 
 
 
3.  SanFran_100b_PCA 
 
 
The 1st 3 Basis Vectors of PCA 
 
  
Scatter plots of the data projection onto 
the 1st 3 IC grouped by the K-means 
classifier results (5 classes) 
 
 
Scatter plots of the data projection onto 
the 1st 3 IC grouped Dave’s unmixing 
classifier results (15 classes) 
 
 
4.  SanFran_100b_ICA  
 
 
The 1st 3 Basis Vectors of ICA 
 
 
3-D scatter plot of the 1st 3 IC data 
grouped by K-means classifier results 
 
 
Scatter plots of the data projection onto 
the 1st 3 IC grouped by the K-means 
classifier results (5 classes) 
 
 
Scatter plots of the data projection onto 
the 1st 3 IC grouped by Dave’s unmixing 
classifier results (15 classes) 
 
  
5.  Navada_50b_PCA 
 
 
The 1st 3 basis vectors of PCA 
 
PC1: 89.996%, PC2: 6.35%, PC3: 2.16% 
 
 
Scatter plots of the data projection onto 
the 1st 3 PC grouped by the K-means 
classifier results (7 classes) 
 
 
6.  Navada_50b_ICA 
 
 
Scatter plots of the data projection onto 
the 1st 3 IC grouped by the K-means 
classifier results (7 classes) 
 IV. Color mapping strategies 
 
1.  Conventional method 
 
A common method for presenting PC images in a pseudocolor display is to directly map the first 
three PCs into an RGB image as shown below. 
 
SanFran_196b_PCA – directly in RGB 
(normalized) 
 
SanFran_196b_ICA – directly in RGB 
(normalized) 
 
SanFran_100b_PCA– directly in RGB 
(normalized) 
 
SanFran_100b_ICA– directly in RGB 
(normalized)  
Navada_50b_PCA– directly in RGB 
(normalized) 
 
Navada_50b_ICA– directly in RGB 
(normalized) 
 
 
2.  Perceptual rendering 
 
PCA and ICA-based perceptual visualization strategies were then explored by mapping the first 
three PC or IC to several opponent color spaces including CIELAB, HSV, YCbCr, and YIQ. The 
gray  world  assumption,  which  states  that  given  an  image  with  sufficient  amount  of  color 
variations, the average color should be gray, was used to set the mapping origins. The rendered 
images are well color balanced and can offer a first look capability or initial classification for a 
wide  variety  of  spectral  scenes.    The  resulting  images  in  different  opponent  color  spaces  are 
shown below for each dataset and each dimensionality reduction method. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 2.1 SanFran_196b_PCA 
 
 
 
SanFran_196b_PCA_CIECAM02_opp2 
 
 
 
SanFran_196b_PCA_CIECAM02_pc2 
 
 
 
SanFran_196b_PCA_CIELAB 
 
 
 
SanFran_196b_PCA_HSV_opp2_ClipS 
  
 
SanFran_196b_PCA_HSV_pc1_NormS 
 
 
 
SanFran_196b_PCA_MatlabYCbCr 
 
 
 
SanFran_196b_PCA_yiq 
 
 
 
SanFran_196b_PCA_yuv 
 
 
 
 
 
 
 
 2.2 SanFran_196b_ICA 
 
 
 
SanFran_196b_ICA_CIECAM02_opp2 
 
 
 
SanFran_196b_ICA_CIECAM02_pc2 
 
 
 
SanFran_196b_ICA_CIELAB2 
 
 
 
SanFran_196b_ICA_HSV_opp2_ClipsS 
  
 
SanFran_196b_ICA_HSV_pc2_NormS 
 
 
 
SanFran_196b_ICA_MatlabYCbCr 
 
 
 
SanFran_196b_ICA_yiq 
 
 
 
SanFran_196b_ICA_yuv 
 
 
 
 
 
 
 
 2.3 SanFran_100b_PCA 
 
 
 
SanFran_100b_PCA_CIECAM02_opp2 
 
 
 
SanFran_100b_PCA_CIELAB 
 
 
 
SanFran_100b_PCA_opp1_ClipS 
 
 
 
SanFran_100b_PCA_opp2_ClipS 
  
 
SanFran_100b_PCA_HSV_pc1_NormS 
 
 
 
SanFran_100b_PCA_MatlabYCbCr 
 
 
 
SanFran_100b_PCA_yiq 
 
 
 
SanFran_100b_PCA_yuv 
 
 
 
 
 
 
 
 2.4 SanFran_100b_ICA 
 
 
 
SanFran_100b_ICA_CIECAM02_opp2 
 
 
 
SanFran_100b_ICA_yuv 
 
 
 
SanFran_100b_ICA_CIELAB 
 
 
 
SanFran_100b_ICA_HSV_opp2_ClipS 
  
 
SanFran_100b_ICA_MatlabYCbCr 
 
 
 
SanFran_100b_ICA_yiq 
 
 
 
2.5 Nevada_50b_PCA 
 
 
 
Nevada_50b_PCA_CIECAM02_opp2 
 
 
 
Nevada_50b_PCA_CIECAM02_pc2 
  
 
Nevada_50b_PCA_CIELAB 
 
 
 
Nevada_50b_PCA_HSV_opp1_ClipS 
 
 
 
Nevada_50b_PCA_HSV_opp2_ClipS 
 
 
 
Nevada_50b_PCA_HSV_pc_NormS 
 
 
 
Nevada_50b_PCA_MatlabYCbCr 
 
 
 
Nevada_50b_PCA_yiq 
  
Nevada_50b_PCA_yuv 
 
 
2.6 Nevada_50b_ICA 
 
 
Nevada_50b_ICA_CIECAM02_opp2 
 
 
Nevada_50b_ICA_CIECAM02_pc2 
 
 
Nevada_50b_ICA_CIELAB 
 
 
Nevada_50b_ICA_HSV_opp1_ClipS 
  
Nevada_50b_ICA_opp2_ClipS 
 
 
Nevada_50b_ICA_HSV_pc2_NormS 
 
 
Nevada_50b_ICA_MatlabYCbCr 
 
 
Nevada_50b_ICA_yiq 
 
 
Nevada_50b_ICA_yuv 
 
 
 
 
 V. Conclusion 
 
These document summaries the results of the project. At this stage of the research, we can see 
from the above section IV that the rendered images are well color balanced and can offer a first 
look capability or initial classification for a wide variety of spectral scenes.  
Though the resulting images may be visually appealing, they may assign different hues 
for pixels that are identified as belonging to the same class. Looking at the scatter plots in section 
III of the first three components grouped by classification results, it’s seen that both PCA and ICA 
have  the  ability  to  capture  the  cluster  structure  in  the  dataset  by  just  using  the  first  few 
components  which  can  be  used  prior  to  cluster  analysis,  e.g.  before  performing  K-means 
clustering to determine a good value for K. A good visualization technique based on PCA or ICA 
should be able to illustrate this cluster feature. According to the research on the perception and 
understanding of different color attributes [Zhang and Montag], we desire a different hue for 
each  class.  Therefore,  further  research  will  be  to  develop  supervised  methods  by  examining 
scatter  plots  of  PCA  or  ICA  data  and  the  corresponding  classification  map  with  the  goal  of 
showing  a  certain  class  with  a  certain  hue  while  maintaining  a  good  hue  separation  among 
classes. 
The usefulness of these visualization strategies may be evaluated and compared with other 
visualization techniques found in the literature by psychophysical experiments.  
VI. Publication 
 
Results  from  the  work  has  been  submitted  to  the  SPIE  Defense  and  Security  Symposium, 
Conference on “Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral 
Imagery”, April 2006. 
VII. Acknowledgment 
 
We would like to take this opportunity to thank Kodak for their financial support for this project. Reference 
 
1.  Beauchemin, M. and Fung, K. B., Intensity-Hue-Saturation Color Display Transform For 
Hyperspectral data. The 21
st Canadian Symposium on Remote Sensing, Ottawa, Ontario, 
Canada, 21-24 June 1999. 
2.  Buchsbaum,  G.  and  Gottschalk,  A.,  1983.  Trichromacy,  Opponent  Colors  Coding  and 
Optimum Color Information Transmission in the Retina. Proc. R. Soc. Lond. B, Vol, 220, 
pp. 89-113. 
3.  Du, H., et al., Band Selection Using Independent Component Analysis for Hyperspectral 
Image Processing, Dept. of Elec. And Comp. Engineering, University of Tennessee. 
4.  Tyo, J. S., Diersen, D. I., and Olsen, R. C., Principal-Components-Based Display Strategy 
for Spectral Imagery, IEEE Transactions on Geoscience and Remote Sensing, Vol.41, No.3: 
708-718. 
5.  Zhang, H., Montag, E. D., How Well Can People Use Different Color Attributes? The 12th 
Color Imaging Conference, Scottsdale, AZ, 2004. 
6.  Wachtler, T., Lee, T. and Sejnowski, T., 2001. Chromatic Structure of Natural Scenes, J. 
Opt. Soc. Am. A/Vol.18, No.1: 65-76. 
7.  http://eol.usgs.gov/hyperion.php 
8.  http://eol.usgs.gov/sampleurban.php 
 

Visualization of High-dimensional Remote-Sensing Data Products

http://www.cis.rit.edu/microgrants/2005/Zhang_report.pdf

Visualization of High-dimensional Remote-Sensing Data Products

Abstract

Similar works

Full text

Available Versions

CiteSeerX