Klasyfikacja obiektowa narzędziem wspomagającym proces interpretacji zdjęć satelitarnych
Wybrane pełne teksty z tego czasopisma
Object-oriented classification as a tool supporting the process of satellite image interpretation
This paper presents the works performed during realisation of a project, which enabled designing an automatic support method of satellite image interpretation of land cover forms. The method is based on object-oriented classification and it comprises five basic stages: image segmentation, classification, generalisation, conversion of the classification images into vector format, verification of the classification using the method of visual interpretation. Based on a study area of nearly 800 sq. km, rules of object-oriented classification of ASTER satellite images were defined. Object-oriented classification was carried out with a division into 19 classes of land cover and land use. The result of classification was generalised using a working unit of 4 hectares and 1 hectare for water and build-up areas (working units are connected with the scale of 1:50000). Next, raster classification images were converted into vector format. The polygon edges of the vector layer were smoothed in order to make them more similar to the borders identified during visual interpretation. Assessment of classification was performed in order to verify correctness of classification codes and borders identified during visual interpretation. To this end, the procedure used in the CORINE 2000 programme was applied. Interpretation resulted in obtaining information about the differences between classification and interpretation. On the basis of these results, it was possible to precisely specify the accuracy of classification of all classes (within the entire study area) and to create an accurate database of land cover and land use. In the process of object-oriented classification, diverse classification criteria were applied. The metod of classification of mixed forest and apartment blocks is particularly interesting: mixed forests were classified as deciduous or coniferous forests characterised with high non-uniformity, while apartment blocks were identified according to shadows of high buildings. During generalisation of the images, only 1.4% of the study area was changed, which indicates that satellite image segmentation was performed properly. Total accuracy of classification was over 86% and half of the classification mistakes occurred as a result of the fact that an image was taken in spring. The suggested method may accelerate interpretation of land cover and land use even by 50% and in some cases it may even replace visual interpretation. The condition for the method to be effective is defining the rules of object-oriented classification for all types of satellite images, as it was done for the ASTER image. The rules of classification do not necessarily have to cover all classes of land cover (sometimes it may even be impossible). Correct automatic identification of even a few classes will accelerate the process of land cover database creation.
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