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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
3.3 Geometry correction result 
Based on TM data (adjusted by using 1:100,000 topographic 
maps), image geometry correction was made. 24 scenery TM 
CCD images (Fig.3) were necessary to carry on the comparison 
correction. In the correction process, used neighbour element 
resample, constant area division conical projection, and applied 
national unified central meridian and the double standard 
parallel, chose 397 control points(road junction, rivers junction 
and obviously object points). The mountainous area had the 
geometry distortion slightly, increased the control dot density. 
Because there was not obvious terrain feature in the Bohai Sea 
area, no point was chosen. After finishing image correction, 
mountainous area error was between 1.5-2.5 elements; plain 
area error was in 1-2 elements. Through contrast the same 
object point between remote sensing data and the topographic 
diagram, computed plane dimension absolute error to confirm 
“Beijing 1” Micro-satellite data precision. Image spot position 
error was in 2.02%, could satisfy the application request. 
Fig.2 "Beijing 1" Micro-satellite spectrum characteristic of 
different land types 
Fig.3 Contrast image Coverage area between “ Beijing 1 ” 
Micro-satellite image and TM 
3.4 Classification result 
Between forest land and lawn, construction land and sand land, 
urban land and countryside residential area, spectral signatures 
were similar. So they were merged separately. Finally 
classification template included 5 kinds: The reservoir, the pit 
pond, the farming, the construction land and sand beach, the 
urban land and the countryside residential area, the forest land 
and the lawn. After the classification, the classification post 
processing was made to obtain the ideal classification effect, 
mainly including colour evaluation, filtration analysis, recode 
and so on. Then scientific classification result was obtained. 
Classification chart was as Fig.4. 
3.5 Classification precision appraisal 
By computation, classification precision was obtained as table 2: 
Classification 
Overall 
Kappa 
method 
precision 
coefficient 
Maximum Likelihood 
89.45% 
0.8436 
Minimum Distance 
73.93% 
0.6240 
Mahalanobis Distance 
84.82% 
0.7848 
Tab. 2 Classification precision contrast in different method 
We can see that classification precisions were high in maximum 
likelihood and Mahalanobis methods. Classification precision 
was low in the minimum range. The partial reasons were 
intrinsic flaws of the algorithm. The minimum range method 
had not considered mobility of the land use type. For instance, 
the urban land image element difference was very big. The 
distance was big to the template's mean value. Then, some 
elements that belong to the urban land use type were possibly 
divided to other categories by mistake. On the contrary, 
regarding some types that internal change was small, such as 
reservoir and pit pond, possibly classification elements were 
excessive, we divided them to the type that did not belonging to 
the type. 
Because of many kinds of factor limit, for example, phase and 
same spectrum different things, three methods all had the 
phenomenon that terrain feature was divided by mistake. 
Spectral signatures between partial forest land and central city, 
reservoir, pit pond were extremely similar, several categories 
had wrong classification phenomenon. The countryside 
residential area assumed non-rule geometrical shape, and 
distributed periphery the farm land. In classification cluster 
processing, some land types that were smaller than 2 elements 
were merged to neighbour big category. Therefore, individual 
countryside residential areas were divided to the farm land type 
by mistake. 
4. CONCLUSION 
The study indicated that “Beijing 1” Micro-satellite CCD data 
has the certainly application value in the land use research field. 
“Beijing 1” remote sensing image coverage is broad. Wide 
range monitor has obvious superiority. The time resolution is 
high, on the aspect of land use dynamic monitor, application 
potential is huge. 
Therefore the data have usability in land use research area, can 
be used in land use/cover, resources investigation and research, 
become one new data resource which the remote sensing data 
renew. 
In future, image geometry correction needs to study further, 
according to its own characteristic. “Beijing 1” Micro-satellite 
data quantity is bigger; the geometry correction process needs 
more time-consuming. So When guaranteeing on high 
correction precision, it is necessary to guarantee quick 
processing, the operating speed, and reduce gamma controller 
change as far as possible. So the new request was set about 
geometry correction method.