International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
  
Red: Band 110 (850nm). 
Blue: Band 35 (550nm) 
Figure 3. The RDACS dataset , September 7, 1999 
including the Pixel Purity Index (PPD, an n-Dimensional 
Visualizer, Spectral Angle Mapper (SAM) and Binary 
Encoding were utilized to map the study datasets. 
For detailed classification, the n-Dimensional Probability 
Functions (nPDF) approach was used (Cetin, 1990; Cetin 
and Levandowski, 1991; Cetin ef al, 1993). The nPDF 
techniques is an interactive 
  
Figure 4. a) Gray-scale placards, b) RDACS imafe showing 
the location of the placards, c) GPS measurement, d) 
sycamore leaves; healthy and under stress, and e) Full range 
(350-2500nm) field spectroradiometer used in this study 
image analysis technique, which overcomes many of the 
inherent limitations of traditional classifiers. The techniques 
has applications in three broad areas: data visualization, 
enhancement and classification. For data visualization, nPDF 
1309 
LBL Overstory Vegetation Spectra 
Reflectance (%) 
(nm) 
  
Figure 5. Spectra of the vegetation species in LBL 
provides a method for transforming multiple bands of data in 
a predictable, and  scene-independent way. These 
transformations may be designed so as to enhance a particular 
cover-type, or to give the best visual representation of the 
multi-band image data. Spectral frequency plots of the nPDF 
components give a spectral view of data distribution that can 
be used to investigate the number and distribution of spectral 
classes in a high dimensional data set. In addition, these plots 
are used n a non-parametric classification of the image for 
discrimination of discrete classes, as well as for classes that 
are mixtures at the sub-pixel scale. In a mixed deciduous and 
coniferous forest an nPDF Deciduous Forest Index showed a 
high correlation with percent deciduous vegetation determined 
from field surveys. 
The nPDF approach may be explained using a cube model. A 
generalized distribution of highly correlated digital remotely 
sensed data in three dimensional feature space is shown in 
Figure 1. In three-dimensional feature space the feature 
vector is defined by X=[ x1,x2,x3]. The location of a point 
within the range of the total possible measurement space can 
be described by the distances to the two corners of the cube 
shown in Figure 6. They are: 
2 2 2.12 
Di = (x, + X x y (1) 
Zu 2 2 A2 
D5- [x *XQ,t(R-x) ] (2) 
For the multi-dimensional case, the feature vector is defined 
by X=[x1,x2.x3.....x,], where n is the dimension of the data 
and R is the maximum possible range of the data (255 for 8 
bit data.) When a hyper-dimensional cube is used, the vector 
magnitudes (the distances to the two corners) for n- 
dimensional data are: