International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
181 
Landcover and 
Landuse Classes 
Classification based 
on Spectral bands 
Classifications based on Spectral 
and Textural bands from matrix 1 
Classifications based on Spectral 
and Textural bands from matrix 2 
Crop 1 
Crop 2 
Trees 
Grass 
Parks 
Apartment-block areas 
Low density urban areas 
High density urban areas 
Water 
cl 
c2 
c3 
c4 
c5 
c6 
c7 
c8 
c9 
90 
82 
86 
90 
82 
88 
96 
88 
88 
78 
80 
84 
84 
84 
90 
96 
92 
82 
98 
90 
96 
92 
90 
94 
96 
90 
92 
98 
88 
94 
94 
92 
96 
94 
94 
80 
68 
76 
80 
85 
78 
80 
92 
94 
78 
68 
72 
76 
78 
74 
84 
96 
96 
84 
64 
68 
72 
78 
72 
86 
90 
94 
86 
62 
70 
74 
76 
74 
78 
92 
94 
90 
98 
90 
94 
92 
90 
98 
96 
94 
80 
Total 
80.4 
82 
84 
84.2 
81.7 
88.2 
94.2 
92.9 
84.4 
Table 3. Classification accuracy (%) for different classification methods and window sizes. 
4. CONCLUSIONS 
The classification accuracy with the new procedure (termed 
procedure 3) is 94.2%, 10% higher than the accuracy achieved 
using procedures 1 and 2. This reveals that textural features 
derived from multispectral images are a very valuable source of 
spatial information and an important clue for landcover and 
landuse classification. The grey level co-occurrence matrix for 
textural measure calculations is an important factor, which 
affects the fidelity of textural features. The textural measures 
based on matrix 2 can more effectively reveal spatial forms of 
landcover and landuse types in multispectral images. 
An inappropriate window size can reduce the classification 
accuracy, and the window sizes of 5 x 5 and 7x7 can be 
considered, based on the performed tests, as the appropriate 
ones for this set of landcover and landuse categories. 
The new procedure is particularly suitable for classification of 
images containing complex spectral components, like urban 
regions. 
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