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ces (which give an electrical signal output). The readings of
the corresponding meters on the three channels give the spec-
tral features of the pixel on which the input light is focus-
sed.
For various technical reasons, the digitization was done
manually using a grid accurately drawn to scale. Each pixel of
the enlarged color print corresponds to & ground element of
size Tm.x Tm. Fig.2 gives & schematic of the pattern recogni-
tion system.
(b) Classification of Categories, : For each of the categories
listed in Sec.1, training samples were selected on the basis of
the known interpretation of the scene {rie.3).
Fig.l gives the computer plot of the relative spectral
responses of the categories as obtained from the training set,
and Fig.5, the histogram which throws some light on the sepa-
rability or otherwise of the various categories. It is observ-
ed from Fig.lh that the mean values of the spectral features are
different for most of the categories.
The classification algorithm is derived from the maximum
likelihood criterion, on the assumption that the measured (vec-
tor)features for all the categories are normally distributed.
For each category, therefore, the parameters to be estimated
are the mean vector and the covariance matrix.
Out of about 5100 pixels of the color infrared print, the
following constituted the training sets
a) Paddy freshly planted (A1.1) 120
well grown (A1.2) 60
mature (A1.3) 120
harvested (A1. ) 30
b) Sugarcane freshly planted (A2.1) 100
young ratoon (42.2) 80
well grown (A2.3) 60
c) Fallow land (A19 ) 60
"Total samples 630
Mean vectors and covariance matrices for these training
sets have been calculated. These parameters correspond to the
frame under study. However, it is believed that they could be
used for the classification of other frames in the same reel.
The classification results obtained from an application of
the maximum likelihood criterion are compared with the exact
interpretation (Fig.3) of the area under study , in Tables 1-5.
Figures 6-11 are the computer print-outs of the classification
results. As regards the error analysis results, the correct in-
terpretation (Fig.3) was overlaid on the computer classification
and the "misfits" were treated as contributions to the error.