The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part BL Beijing 2008 
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2.2 Method of image quality appraisal 
Image quality appraisal qualitatively analyzed and described 
“Beijing 1” image character by visual detection from some 
aspects, such as texture structure, data continuity, noise 
characteristic, radiation precision as well as interpretation 
ability and so on. 
2.3 Method of land use classification 
Supervised and unsupervised classification methods were used 
to extract the sample. First Initial classification was carried on 
in order to get the training area. Simultaneously, "the same 
thing with different spectrum, the different thing with same 
spectrum" category and region were founded for revision 
classification result. Then by selecting training sample and 
classification function, supervised classification was made, 
finally revised classification result by interpreter and made 
classification precision appraisal. During the supervised 
classification process, in order to contrast classification 
precision, three classification functions were selected. The 
maximum likelihood formula was as follows: 
determining accurate reference category of the sampling point, 
classification precision appraisal was finished. Appraisal results 
showed in confusion matrix. Overall precision and Kappa 
coefficient were extracted from the matrix. The value was 
bigger, classification precision was higher. The detailed flow 
was as follows: 
[Micro-satellite Data Processing! 
lUnsupervised 
Sample Extract 
Building Classification Machine 
1 
1 
[Maximum Likelihood -|—1~Mahalanobis Distance h-l-Minimum Distance 
Supervised Classification 
[Accuracy Assessment I 
D = ln( a e ) - [ 0 .5 ln(| Cov c |)] - 
[ 0 .5 ( A - M C )T (Cov ; 1 )( X - M 
) 
Fig.l Image land use classification flow 
in the formula, D : Weighting distance, C : Some 
characteristic type, X : Element survey vector, M c :Sample 
average vector of type C, Cl c : Percentage probability of any 
element belonging to type C , CoV c : Element covariance 
matrix of type C ,T : Transposition function. The minimum 
range formula was as follows: 
d ( x , M , ) = [ £ ( x k - m ik ) 2 ] 
k = 1 
in the formula: n : Wave band number, ^ :Some characteristic 
t i / n M- j 
wave band, 1 :Some cluster center, 1 : sample L mean, 
Mtk . 7h e ith cen t r e and ^th jonj element value, 
d(x,M„ , , ¡fL 
1 : Distance of the element to the U L 
kind 
M ; 
central( 1 ). The Mahalanobis distance formula was as 
follows: 
D = (X - M C )T (Cov ;')(! - M ) 
in the formula: D • Mahalanobis distance, c : Some specific 
kind, X : Element survey vector, ^ c : Type c template 
Cov r 
average vector, c : Element covariance matrix in type 
c template, T : Transposition function. 
2.4 Method of Classification Precision appraisal 
Using ERDAS software, classification precision appraisal was 
finished. Stochastically 500 spots were extracted. After 
3. RESULTS AND DISCUSSION 
3.1 Visual analysis result 
By visual detection, “Beijing 1” image spectrum information is 
rich. The texture structure is clear. Escaping belt and leaking 
belt phenomenon does not exist. Data continuity is good. There 
is no partially geometry distortion. The forest land, the lawn, 
the urban land, the countryside residential area, the reservoir 
and the pit pond can be distinguished clearly from the image. 
The mountain takes on seal brown (crag bare) and red, scarlet 
red (covered by vegetation). Water body outline is obvious, 
Because of different silt content, takes on different colour. The 
tiny drainage ditch is green. The reservoir and pit pond wer 
dark blue or black along with different depth. In the image 
Beijing built-up urban area outline is clear, but the city street 
texture is fuzzy. The whole interpret situation is good. 
3.2 Spectrum information analysis result 
In order to providing reference for land use computer automatic 
classification research, Get terrain feature spectrum diagram of 
curves (Figure 2), extracted different land spectrum information, 
analyzed image spectral signatures. From the chart, we can see: 
the water spectrum value in the wave band 1 was lower than the 
spectrum value in the wave band 2 and 3. Farming land, forest 
land and lawn were most obvious in wave band 1. The wave 
band 1 was green vegetation's high echo area. The wave band 2 
was vegetation's low echo area. It was sensitive to the plant 
chlorophyll's absorption. Spectrum value of the Sand beach and 
construction land was high and the spectrum information was 
easy to get. But the urban land and the countryside residential 
area's curve characteristic were similar, and the forest land and 
the lawn curve type were similar. When making computer 
automatic classification, it was not easy to differentiate the 
information.