Geoscience Reference
In-Depth Information
Consistency as a Function of Producer's
Accuracy for a Range of Class Proportions (f)
1
0.8
f = 0.1
f = 0.5
f = 1.0
f = 10.0
0.6
0.4
0.2
0
0.5
0.6
0.7
0.8
0.9
1
Producer's Accuracy
Figure 10.1
Relationship of classification consistency as a function of producer's accuracy for a range of class
proportions (f). The four cases shown span the range of forested and nonforested class proportions
encountered in scenes of the Laurentian Great Lakes watershed.
User's Accuracy as a Function of Producer's
Accuracy for a Range of Class Proportions (f)
1
0.8
f = 0.1
f = 0.5
f = 1.0
f = 10.0
0.6
0.4
0.2
0
0.5
0.6
0.7
0.8
0.9
1
Producer's Accuracy
Figure 10.2
User's accuracy as a function of producer's accuracy for a range of class proportions (f). The four
cases shown spanned the range of forested and nonforested class proportions encountered in
scenes of the Laurentian Great Lakes watershed.
•
Each Landsat scene is independently classified and composited with other scenes to generate a
final large-area LC product. This approach was labor intensive and is suitable primarily for synoptic
mapping (i.e., categorization into a few broad classes). However, it did have a number of important
practical advantages: image information content could be thoroughly exploited, and consistency
analyses were undertaken on each scene by comparing its classification with those of its nearest
four neighbours (cross- and along-track). Thus, regional variations in classification accuracy, arising
from interscene quality differences and spatial diversity in class proportions, were monitored at
the scene level.
•
Scene classification was achieved through unsupervised spectral clustering (K-means algorithm,
150 clusters), followed by cluster labeling. For synoptic mapping (i.e., < 10 classes), each class
was described by a number of clusters (5-50). Cluster-based classification had some important
ramifications for accuracy considerations, including: (a) the true “unit of classification” was the
cluster, since it was at this level that label decision-making occurs; (b) since each class was
represented by a number of clusters, we did not expect that the labeling of each cluster would be
equally reliable; and (c) if consistency was evaluated at the cluster level and not at the “conventional”
