training fields parallel pipes maximum likelihood classifier class 11. supervised classification
TRANSCRIPT
Training Fields
Parallel Pipes
Maximum Likelihood Classifier
Class 11. Supervised Classification
Unsupervised classification is a processof grouping pixels that have similar spectral values and labeling each group with a class
Definition
Supervised classification is to classify animage using known spectral information foreach cover type
1. Training Fields (minimum spectral distance)
A sample area for estimating representative spectral statistics,or spectral signatures.
A seed-pixel approach can be used (page 137, Verbyla) according to the minimum distance classifier
Verbyla 7.0
Two-band image
AB: Aspen/Birch
SM: Sedge/Medow
Lillisand & Keifer 7.0
2. Parallelpiped classifier
Define max/min for each band for each class
If a class has normally distributed spectral valuesthen 95% of pixels are within mean±2 standard deviations, i.e.,
Minimum = mean-2×SDMaximum = mean+2×SD
Max/min can be adjusted according to needs
Step-wiseparallelpipes
3. Maximum likelihood classifier
From the training field, create contours of equal likelihood for each class. The highest likelihood for a candidate pixel determines the class of the pixel
Single-band example
From training fields for cattail (CT) and smartweed (SW)
Mean digital value Standard deviation
()
Number of pixels
CT 30 5 100
SW 20 5 100
Class 12
Assessment of classification Accuracy
Error Matrix (confusion matrix)
User’s AccuracyProducer’s Accuracy
Overall AccuracyKappa Statistics
Error MatrixGround Truth
1 2 3 4 5 Row total
1 40 0 0 3 0 43
2 0 30 12 0 1 43
3 0 3 25 0 2 30
4 2 0 0 50 0 52
5 0 0 0 0 32 32
Column total
42 33 37 53 35 200
Pre
dic
ted
class
class
Verbyla 8.0
Overall Classification Accuracy
It is the total number of correct class predictions(the sum of the diagonal cells) divided by the total number of cells.
In this case, it is (40+30+25+50+32)/200 =88%
Producer’s and user’s accuracy by cover type class
Class Producer’s Accuracy User’s Accuracy
1 40/42=95% 40/43=93%
2 30/33=91% 30/43=70%
3 25/37=68% 25/30=83%
4 50/53=94% 50/52=96%
5 32/35=91% 32/32=100%
Kappa Statistic
KHAT=Overall Classification Accuracy – Expected Classification Accuracy
1 – Expected Classification Accuracy
The expected classification accuracy is the accuracy expected based on chance,Or the expected accuracy if we randomly assigned class values to each pixel. In this case (see the next slide), it is (1806+1419+1110+2756+1120)/40,000=21%
In this case, KHAT=(0.88-0.21)/(1-0.21)=0.85
Products for KHATGround Truth
1 2 3 4 5 Row total (error matrix)
1 1806 1419 1591 2279 1505 43
2 1806 1419 1591 2279 1505 43
3 1260 990 1110 1590 1050 30
4 2184 1716 1924 2756 1820 52
5 1344 1056 1184 1696 1120 32
Column total (error matrix)
42 33 37 53 35 200
Pre
dic
ted
classclass