d'Agriculture de Haute-Saone), M. Bruneau (Centre d'Etude Geogra- 
phique et Ecologique Tropical), M/s Kilian and Raunet (Institut 
de Recherche Agronomique Tropical). ICAR processing has been ap- 
plied to eight test sites: three in France, four in Africa and 
one in Asia, analysing different problems in each place. 
DEFINITION OF REGIONS 
  
  
The smoothing processes often used to generalise the result of a 
classification do not preserve or take into account the spatial 
heterogeneity of the original image. Only textural analysis 
registers spatial heterogeneity, its one drawback being the diff- 
iculty of simultaneous work on several channels. Hence, this paper 
tests a process which permits one to use the spatial heterogeneity 
of the image in several channels. 
1) Classification 
The starting point of the work is a four channel image. This 
is reduced to a single channel by classifying each pixel.In most 
cases a supervised maximum likelihood classification is used. 
The number of classes is between 7 and 13. 
2) Heterogeneity/Homogeneity of Regions 
Whichever classifier is used the result appears noisy, both 
because of the way the data are recorded and because of the natural 
heterogeneity of landscape. This natural heterogeneity is smoothed 
by the scanner as a function of the pixel size. Processing techni- 
ques such as smoothing, labelling relaxation or filtering suppress 
local heterogeneity e.g. within fields, but present techniques 
do not deal with regional heterogeneity e.g. field patterns. 
In a visual analysis one attempts to define regions by the 
similarity of patterns of units, hence, in digital analysis, one 
defines statistical parameters to characterise these regions. 
These parameters are the percent of pixels of each class present 
computed relative to all the pixels of a scanning window. 
If we take: C as the number of classes ( 1$€i or j&C ) 
Ni as the number of pixels of class i computed 
at a given moment in the window, 
then Pi, the percent of pixels of class i in the window, is: 
  
Pi socNi/Sj Nj 
The numerically computed parameter to define a region is a set 
of Pi: 
(Pl;P2,P3,P4;...Pi;..5.5Pc) 
known as the regional signature. 
Given the definition of Pi, the number and the nature of the 
classes can be chosen in many different ways. For example, one 
may decide to compute statistics of agricultural land cover for 
each point relative to all points ( excluding all other classes 
such as forest, urban area, water,....) This give a relative den- 
sity of agricultural land, and the technique can be repeated for 
forest, water, etc.. In addition multiple computations may be 
done to produce the density of individual crops, e.g. wheat, 
maize, sorghum. f 
This process is called MULTIDENSITY. 
3) Empirical Definition of Regional Types 
Each point is now characterised by a multidensity vector of Pi. To 
define regions from this numerical matrix, we need multidensity 
vectors for reference. These are obtained from the computation of 
Pi within given areas, each one representing a type of landscape 
to be identify. This set of multidensity reference vectors can 
  
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