Geoscience Reference
In-Depth Information
The restricted two-class scenario (i.e., classes A and B) provides useful insights for those classes
within a larger class mix whose labeling accuracy is limited primarily by pairwise class confusion.
In this case, Equation (10.1) reduces to:
C
= [f P
P
+ P
P
]/[f P
+ P
]
(10.3)
1A
1AA
2AA
1BA
2BA
1AA
1BA
where f is the ratio of numbers of true class A to true class B pixels. That is:
f = N
/N
(10.4)
A
B
It can be seen that consistency is a function not only of the producer accuracies but also the relative
class proportions. Similarly, user accuracy can be expressed as a function of producer accuracy
and f. For example:
Q
= f P
/[f P
+ P
]
(10.5)
1A
1AA
1AA
1BA
If the two classifications are derived from similar data sources (e.g., scenes from the same sensor),
each scene will typically exhibit similar producer accuracies (i.e., P
= P
= P
, etc.). In this
1AA
2AA
AA
instance, consistency and user accuracy will be the same for each scene:
C
= C
= C
= [f P
AA
2
+ P
BA
2
]/[f P
+ P
]
(10.6)
1A
2A
A
AA
BA
and
Q
= Q
= Q
= f P
/[f P
+ P
]
(10.7)
1A
2A
A
AA
AA
BA
We have examined the relationships of consistency and user's accuracy as functions of pro-
ducer's accuracy and f for a range of parameters applicable to the Laurentian Great Lakes region
in which LC has been classed as either forest or nonforest. Producer's accuracies in the range 0.5
to 1 need only be considered since 0.5 corresponds to random class assignment. Also, for this level
of stratification, we would expect high producer's accuracy performance (e.g., > 0.8 with Landsat
Multispectral Scanner (MSS) data). Finally, in the Great Lakes region, f varies dramatically from
approximately 0.1 in the agricultural south to 10 in the north for forested land and vice versa for
unforested land.
Figure 10.1 and Figure 10.2 illustrate the relationships of consistency and user accuracy with
producer's accuracy, respectively, for f values ranging from 0.1 to 10 and a nominal class B
producer's accuracy of 0.8. These results are typical of a range of realistic cases. From an inspection
of these plots we can draw a number of conclusions: (1) both consistency and user's accuracy
increase monotonically with producer's accuracy, suggesting that consistency is an indicator of
classification accuracy performance and (2) consistency and user's accuracy exhibit similar sensi-
tivities to f. We hypothesize that consistency can be employed as a “surrogate” of user's accuracy
to monitor variations in accuracy at scene-level spatial scales.
10.3 USING CONSISTENCY WITHIN A CLASSIFICATION METHODOLOGY
Our approach for applying consistency measures is dependent on the specific algorithms and
methodologies employed for our study area. The following discussion addresses key aspects of our
Great Lakes LC methodology and how they incorporate consistency and address our accuracy
objectives. Figure 10.3 illustrates the overall data processing flow.
