The development of interactive decision boundary determination method in the feature space of remotely sensed data
Case Study
The test site was determined in Fukuoka city and vicinity Fukuoka is the largest city in Kyushu island and the core city is south western Japan with the population of one million facing to the original image the specification of the test data is as follows
Sensor : LAND SAT TM
Scene 1D : PATH 112 row 37
Date : MAY 12 1986
Resembling : APPROX 20mX20m 1/50 National Standard Grid
Date Size : 1000 X 1000 Pixels
- Principal component transformation
Principal component was calculated from six bands of TM original bands excluding and their contributing ratio. The two dimensional histogram on the first two principal component plane obviously this histogram consists of three big clusters one may easily draw decision boundary between three classes on this image.
- Collection of training samples and decision boundary setting shows some exampled of training sample collection.
Fig.2 Original TM Image of Test Site
Fig.3 Two-dimensional Histogram on the first two Principal Component Plan
Fig.4 Training Sample collection
Table.1 Principal Components and their contributing ratio
| |
P.C.1 |
P.C.2 |
P.C.3 |
P.C.4 |
P.C.5 |
P.C.6 |
Eigen Vector1
channel. 1
2
3
4
5
6
7 |
.4090 |
-4090 |
-.2848 |
.6774 |
.3061 |
-.1783 |
| .4343 |
-.2185 |
-3164 |
.-2838 |
-0976 |
.7573 |
| .4337 |
-.2330 |
-1711 |
-5635 |
-1639 |
-6197 |
| .2955 |
.7986 |
.1794 |
.174 |
1891 |
-0831 |
| .4227 |
.307 |
.4398 |
.1919 |
.7042 |
.0481 |
| .4359 |
-.0036 |
.6293 |
.2722 |
-.5817 |
.0390 |
contributing
ratio
Accumulated |
.8328
.8328 |
.1411
.9728 |
.0189
.9927 |
.0044
.9971 |
.0019
.9989 |
.0011
1.0000 |
Selected classes are high density urban area low density urban area agricultural fields grass fields forestry and water 10 shows the distribution of training samples over the histogram respectively bu referring these distribution aforementioned three clusters were cleared to be corresponding to water forest lie urban classes classes agricultural fields and grasses fields lie in between forestry and urban. The rough setting of decision boundary referred to the training sample distribution the classified result in to those six month classes the classified result by ordinary maximum classification likeihood method the same training samples.
- Editing of the decision boundary
By Comparing 12 13 we can see several differences as far water classes is concerned ML method is seemed to be under classified as unexpected islands appeared in the sea on the contrary this method is likely to be over classified as a break water disappeared the reason why ML is under stand is supported to be that training samples are too homogenous to represent whole to be that class evidently seen in we set the decision boundary for water class too large as seen in fig 11 by looking forestry class the result of this method is less noisy than the result of ML method this smoothing effect of this method appear in other classes .
Fig.5 Training samples of H-Urban Area |
Fig.6 Training samples of L-Urban Area |
Fig.7 Training samples of agricultural fields |
Fig.8 Training Samples of Grass Fields |
Fig.9 Training Samples of forestry |
Fig.10 Training samples of water |
In order to salvage sank break water training samples are taken from the pixels of the water break and displayed the histogram as than decision boundary for water and urban are corrected so that the area corresponding to the training samples is saved from water class to urban class corrected decision boundary and the associated classified result.
