The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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Target region 
coverage classification based on Aster image. (Han.2006) 
It is the region about 12km toward east & west and about 11km 
toward south & north among construction target region for 
multi-functional administrative city (Seijong-si) of Korea. 
(Ficture 2) It is total 72.996 in area including Yeongi-gun (3 
myeon and 28 ri) and Gongju-si (2 myeon and 5 ri) in aspect of 
administrative region. 
ULtLat) S6.59Ü!ÊQa UL(LOft) 12?. I ?35361 2 
UWLafi 36.4820849 UR(Lon) 127.44257221 
5 ROI, vegetation (cultivation), road, water, forest, residence 
class is set using maximum likelihood algorithm and 35 training 
data sets were designated. Overall accuracy of 94.27% and 
Kappa coefficient of 0.86 were acquired. 
Figure 2. Testing area 
3. EXPERIMENT 
3.1 Image 
Aster image used in pixel based classification is most high in 
resolution and it is VNIR image providing 15m resolution in 
visible range and near infrared and multi-purpose satellite 
KOMPSAT2 image of Korea is used for classification of high 
resolution image. The satellite was launched on Jul. 28th 2006 
and MSC with capacity for panchromatic lm, multi-spectrum 
4m and swath width of resolution 15km in sun synchronous 
orbit of orbital altitude of 685km. Acquired image is *ID: 
msc_071005015048_06336_10811273__PS image taken on Oct. 
5th, 2007 and high resolution 1 m color image is acquired with 
HPF image fusion method. Geometrical correction was process 
of affine conversion selecting the 25 points from 1:1000 
topographic map. Image re-sampling was completed with 
nearest neighbor method. Also, ortho rectification was 
processed suing the lm DEM data acquired from contour line. 
3.2 Pixel Based Coverage Classification 
1) Low Resolution Image Coverage Classification 
Supervised classification was carried out for pixel based 
Table 1 Pixel based classification accuracy 
Overall Accuracy = (73364/77817) 94.27% 
Kappa Coefficient = 0.8664 
Ground Truth (Pixels) 
Class 
Road 
Forest 
Cultivatio 
n 
Water 
Residence 
Unclassified 
0 
0 
0 
0 
0 
Road 
5437 
9 
14 
54 
30 
Forest 
1 
29174 
5 
17 
19 
Cultivation 
104 
77 
23114 
983 
300 
Water 
70 
17 
233 
9367 
59 
Residence 
211 
253 
208 
804 
6272 
Total 
5823 
29530 
23574 
11225 
6680 
Figure 3 Land coverage map using Aster image 
2) High Resolution Image Coverage Classification 
To increase the accuracy of classification using high resolution 
image, object based classification method was applied. Land 
coverage map was drawn up as result of classification to 
compare the accuracy of classification and it is produced as 
raster data. Also, accuracy was compared by producing the land 
used map with GIS technique using land category provided in 
cadastral map and distribution rate for each class. Data 
processing work flow chart is as figure 4. RXD detector was 
used for extracting algorithm. RXD is the algorithm verified 
that it is clearly effective in detecting the opaque and non 
transparent object and dead pixel or line does not have 
influence in detection though it is abnormally generated. 
(Chang et al. 2002) (Reedl.S & X. Yu, 1990) 
Work Flow 
Image registration & Ortho 
rectification ÎGCP & tm OEM) 
Sample extraction 
Ut 3738/9 100.216029.000 
t,fl: 3/0879,100. 219029.800 
Feature extraction algorithm 
High resolution coverage 
classification 
Classifying land use Category [■ 
Grouping 
spectral & Shape based 
Segmentation (resolution) 
Classification 
Grouping 
J 
Themai 
S da 
¿c map 
sses 
7~~~~ 
Figure 4. Work flow of study