A study of land cover classification accuracy for MOS-1 MESSR high and low gain data.
Test site and used data.
There are about 50 km of side lap area between the two paths of MESSR scenes An area of 34 by 28 km in the side lap area between two MESSR scenes 20-69 west and 21-60 east was selected as the satellite in this study as the observation area of MESSR moves by ones path west ward every below which observed the test site in normal and high gain mode one day apart were used for this study.
| 20-69 |
west |
SYSTEM I |
normal gain |
Dec 3. 1989 |
| 21-69 |
east |
SYSTEM II |
high gain |
Dec.3 1989 |
a flat part in the side lap area was selected as the test site in order to avoid the stereoscopy caused by two data seen from two adjacent paths.
Fig 3 MESSR Scenes and test site
Study Method
Study method for the evaluation of land cover classification accuracies for high and normal gain data is shown in fig 4 at first a portion of images covering the test site was extracted each from the original MESSR data they were geometrically corrected using GCPs Ground control points 24 GCPs. Were selected and an affine transformation was used for the geometrical correction and the residual errors were about 0.6 pixel with both images which is considered that the two images register with an error less than 1 pixel training areas for land cover categories were likelihood classification was carried out supervised maximum likelihood respectively for high and normal gain data as the acquisition dates of two data are only on e day different the land cover conditions are considered to be almost same the land cover classification accuracies for high and normal gain data were evaluated by examining the confusion matrices of the training data.
Selection of training and classification of the test site images.
Twelve categories of land cover as shown in table 2 were established in this study the training data which area are sample area representing each category were selected referring geographical maps of the test site mean values of training data of land cover categories are shown in fig 5 it can be seen from that the values of high gain are three times as higher as those of normal gain in band 1.2 and twice as higher as in band 4 and the values of band 3 are same, it shows that the input out put characteristics of high and normal gain shown in fig. 2 are realized correctly A supervised maximum likelihood classification was carried out using the training data selected above for each high and normal gain data the percentage of each category of the classified images through the sets site for high and normal gain data is also shown in table 2.
Table 2 land cover categories selected for the classification and percentage of each category of the classified image through the test site.
| No. |
Category |
Percentage |
| Normal gain |
High gain |
| 1. |
City |
1.3 |
0.9 |
| 2. |
Residential |
18.8 |
16.6 |
| 3. |
Factory |
2.4 |
0.9 |
| 4. |
Bare soil |
3. 8 |
8.9 |
| 5. |
Waste |
23.1 |
2 9.1 |
| 6. |
Lawn |
5 .7 |
4.1 |
| 7. |
Farm 1 |
18. 2 |
1 4.6 |
| 8. |
Farm 2 |
2 .8 |
5.9 |
| 9. |
Paddy field |
18.0 |
13.5 |
| 10. |
Forest |
4.8 |
4.4 |
| 11. |
Mountain |
0.1 |
0.2 |
| 12. |
River water |
1.0 |
0.9 |
