Full text: Proceedings, XXth congress (Part 4)

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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
LANDSAT: 
tx 7 0,1926 « 1,703 and rg — 0,3977 « 1,703 (there is 
no trend in these directions) 
CBERS: 
tv = 0,2828 < 1,703 and tg 7 1,2172. « 1,703 (there is 
no trend in these directions) 
42 Accuracy Analysis 
Accuracy analysis was carried out for both Landsat and CBERS 
images and the results of the estimates of Y nd and Pints 
computed using Equation (3) and (4), the first part of Table 3 
and Table 1, are presented in Table 4. 
  
  
  
  
  
  
  
CLASSES | DIRECTIONS |LANDSAT| CBERS 
A E 1.265.172| 1.317.865 
N 1.387.768| 1.322.864 
B E 455.463 | 474.432 
N 499.598 | 476.232 
C E 316.293 | 329.466 
N 346.942 | 330.716 
  
  
  
  
  
  
Table 4. Estimated values of 7^4, and nl used in the 
accuracy analysis for the Landsat and CBERS images. 
Adopting the critical value of 7? — 36,7412 and considering the 
limit values for the classes A, B, and C as shown in Table 1 for 
the Brazilian Map Accuracy Standards (scale 1:100.000), these 
two images have accuracy equivalent to the class C. 
43 Generated Point Method 
The values of the Standard Error (SE) for the generated point 
method are the same as the Brazilian Map Accuracy Standards, 
for instance, (see Table 1). As this method works with relative 
distances between homologous points, there is no need to apply 
trend analysis to check for the presence of systematic errors in 
the E and N directions. 
The.accuracy analysis was also performed for both Landsat and 
CBERS images using a set of equally spaced homologous 
coordinates in order to check the positional feature accuracy for 
à given confidence level. The results are presented in Table 5. 
  
  
  
  
CLASSES | LANDSAT | CBERS 
A 128,0141 | 441,437 
B 46,0852 158,918 
C 32,0035 110,359 
  
  
  
  
  
Table 5. Estimated values of y^p, used in the accuracy 
analysis for the Landsat and CBERS images 
The critical value of 7^ is 48.2329 and the limit values are given 
In Table 1 for the Brazilian Map Accuracy Standards (scale 
1:100.000). The Landsat image has an accuracy equivalent to 
class B, while the CBERS product does not fit in any specify 
class. 
S. CONCLUSIONS 
Trend analysis indicates that there is no systematic error in any 
direction for the Landsat and CBERS geometrically corrected 
Images at the 9094 confidence level. On the other hand, both 
983 
corrected image have an accuracy equivalent to class C using 
the Brazilian Map Accuracy Standards for a map scale of 
1:100.000. Only the Landsat image fits the class B 
specification, using the generated point method. 
These results show that a considerable amount of research 
needs to be undertaken before the spatial characterization of 
positional and thematic accuracy associated with remote 
sensing data can be adequately reported in standardized format 
and legends. Several techniques for the quality control of spatial 
databases using generic features are adapted to the context of 
remote sensing. However, one drawback for some of these 
approaches is the difficulty in obtaining homologous points in 
both representations. Alternative techniques for overcoming 
such limitations could be the use of a spline Fitted method, in 
which the shape of curves fitted using splines are compared 
instead of isolated homologous control points (Galo et al., 
2001). 
6. ACKNOWLEDGEMENTS 
Preliminary research by Dr. Vieira was supported by the 
Brazilian Research Council (CAPES). The later stages of this 
study were conducted as part of the CNPq/COAIE/Kit Enxoval 
(Project Number 68.0045/01-5) performed in collaboration with 
The University of Nottingham. We are grateful to Logica PLC, 
INPE and SPOT Image for permission to use their images. 
7. REFERENCES 
Arbia, G., Griffith, D., and Haining, R., 1998. Error 
propagation modelling in raster GIS: overlay operations. 
International Journal of Geographical Information Science, 12, 
pp. 145-167. 
Congalton, R. G., and Green, K., 1999. Assessing the Accuracy 
of Remotely Sensed Data: Principles and Practices. Lewis 
Publishers, New York. 
Ferreira, L. F., and Cintra, J. P., 1999. Quantificaçäo de 
Discrepáncias entre  Feigóes  Lineares por  Retángulo 
Equivalente. Brazilian Magazine of Cartography, 51, pp. 1-8. 
Galo, M., Dal Poz, A. P., and Ferreira, F. M., 2001. Uso de 
feiçôes no controle de qualidade em cartografia. In Proceedings 
of the XX CBC - Cartography Brazilian Congress, Porto 
Alegre, 7 - 12 October 2001, 
http://www.cartografia.org.br/200 l/english/index.html (acessed 
10th Oct. 2001). 
Jassen, L. L. F., and van der Wel, F. J. M., 1994. Accuracy 
assessment of satellite derived land-cover data: a review. 
Photogrammetric Engineering and Remote Sensing, 48, pp. 
595-604. 
Jensen, J. R., 1986, Introductory Digital Image Processing — A 
Remote Sensing Perspective. Englewood Cliffs, NJ, Prentice- 
Hall. 
Lunetta, R. S., Congalton, R. G., Fenstermaker, L. K., Jessen, J. 
H., and Mcgwire, K. C., 1991, Remote sensing and geographic 
information system data integration: error sources and research 
issues. Photogrammetric Engineering and Remote Sensing, 57, 
pp. 677-687. 
Merchant, D. C., 1982. Spatial accuracy standards for large 
scale line maps. In Proceedings of the Technical Congress on 
Surveying and Mapping, 1, pp. 222-231. 
 
	        
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