rx X me. ze 
| 
its 710, TEXTURE ANALYSIS 
(data 
lution Although texture is one of the more 
important characteristics used by a human 
ar images to identify objects of interest in an 
igital image, machine classification of digital 
,, each images is frequently based solely upon 
lar image spectral features which describe the 
dar overall tonal variations within an image. 
des Textural features, however, describe the 
jf the local spatial distribution of tonal 
) for values within an image (Ref.9). In spite 
of tremendous advances in the field of 
nt set computer technology during the last de- 
1 and Figure 5. "Texture" Image From cade, the cost of N EEE digital 
the ir Data Set 709 (X) image texture on an general-purpose 
1e computer is still very high. The appli- 
1 order cation of classification procedures in- 
NS volving image texture to remote sensing agricultural surveys has remained pri- 
could marily a research topic due to the magnitude of the computational resources 
required to achieve a reasonable throughput. With the advent of SAR, which can 
igures achieve a much finer resolution than a real aperture radar, as well as recent 
of advances in digital computation devices, there is hope to exploit texture in- 
formation in support of agricultural inventories. 
same Reviewing the literature on texture models (Refs.9-11) there is strong 
r, supportive evidence that the Spatial Gray Level Dependence Method (SGLDM) is 
center representative of the best texture algorithms currently available. Consequently 
Mta in I selected the SGLDM-Approach as a starting point for my research. 
| and Initially, it is assumed that the texture context information in an image is 
rical contained in the overall or "average" spatial relationship which the gray tones 
st in the image have to one another. This relationship can be characterized by the 
i- south set of co-occurrence matrices P (i,j) d,0 whose i,j*^ element is the relative 
on down frequency with which two neighboring picture elements separated by distance d 
Irns in a direction 9 occur in the image, one with gray tone i and the other with 
gray tone j. 
|ternate : à É 
The computation of the texture measurement values involved three steps. First, 
the "Texture Image" (Fig.4) was level-sliced into a smaller number of intervals 
in order to ease the computational burden. Since the majority of the data values 
lay between 100 and. 160 with a mean of y - 128 and a standard deviation of 
c- l0, I chose to quantize into 8 intervals of one standard deviation around 
tively the mean. The next step consisted of calculating the co-occurrence matrices over 
appear a moving circular window of radius 5 pixels using a displacement distance of 
e same d = 2 and four different angles, 8 = 0°, 45°, 90°, and 135°. Textural features 
of these (scalar functions) were then computed from these co-occurrence matrices at 
ition each location in the 1024 x 1024 image. In order to obtain texture measures 
gery which are insensitive to the orientation of the sensor, I chose to use the 
ly average value of a texture measure over all four angles (i.e., from each of the 
jing four 8 by 8 co-occurrence matrices developed at each possible location of the 
ize moving window). The scalar function which was employed in this investigation 
ent is called "Contrast" (Refs.9, 11) or "Inertia" (Ref.12). Its mathematical de- 
I finition is described in Eq. 3. 
5). 
235 
  
CES E a e acil ———— 
nd M ei N NE FE SS AR