Full text: Proceedings, XXth congress (Part 7)

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: 
 
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.