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2004 | 11 | 349-358

Article title

Examples of object-oriented classification performed on high-resolution satellite images

Content

Title variants

Languages of publication

Abstracts

EN
Information about the types of land cover and its use is obtained by the visual interpretation of the color composite of satellite images or by the use of automatic classification algorithms. For obvious reasons, the automatic classification methods make it possible to obtain information quicker and much faster than the traditional interpretation method. The commonly used automatic methods of satellite image classification, based on supervised or unsupervised classification algorithms, are the most accurate when used with low resolution images. In the case of images with 1-meter-sized pixels, showing a diversity of land cover forms, it is not possible to obtain satisfactory results. New classification techniques, based on object-oriented classification algorithms, have been developing for a couple of years now. In contrast to the traditional methods, the new operating procedure does not involve the classification of single pixels, but of entire objects, into which the content of the satellite image is divided. Aside from the spectral values of the pixels, the shape of the objects created by the pixels and the relationships between the objects, are also considered during the analysis. Similar to visual interpretation, variation in the texture of the image can also be taken into account in this case. The aim of this article is to present the possibility of using high density satellite images in object-oriented classification. The classification presented is that of a high-rise built area in Wrocław and of bridges on the Vistula River in Warsaw.

Year

Volume

11

Pages

349-358

Physical description

Dates

published
2004

Contributors

References

  • B a u e r T., S t e i n n o c h e r K., 2001, Per-parcel land use classification in urban areas applying a rule-based technique, GIS, vol. 6.
  • B r a n d t T., M a t h e r P.M., 2001, Classification methods for remotely sensed data, Taylor & Francis.
  • Corine land cover - Technical guide, 1993.
  • eCognition User Guide 2001, Definiens Imaging.
  • d e K o k R., B u c k A., S c h n e i d e r T., A m m e r U., 2002, Modular project design in object oriented analysis, GIS und Fernerkundung: Neue Sensoren - Innovative Methoden, Wichmann Verlag, Heidelberg: 33 - 42.

Document Type

Publication order reference

Identifiers

Biblioteka Nauki
2029415

YADDA identifier

bwmeta1.element.ojs-issn-0867-6046-year-2004-volume-11-article-fc0fd141-27f4-3623-ab23-424e88bf1415
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